{
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  "generated_at": "2026-11-09T16:05:25Z",
  "repositories": [
    {
      "id": "lerobot",
      "name": "LeRobot",
      "repo": "huggingface/lerobot",
      "fields": [
        "로봇"
      ],
      "techniques": [
        "AI"
      ],
      "tags": [
        "Python",
        "모방학습",
        "로봇 데이터"
      ],
      "summary_ko": "로봇 학습용 데이터셋·모델·도구를 제공하는 라이브러리.",
      "design_note": "수집할 로봇 시연 데이터와 학습 정책의 구성을 비교하는 출발점.",
      "operations_note": "실물 적용 전 하드웨어 지원과 데이터 형식, 안전한 평가 절차를 확인해야 한다.",
      "full_name": "huggingface/lerobot",
      "html_url": "https://github.com/huggingface/lerobot",
      "description": "🤗 LeRobot: Making AI for Robotics more accessible with end-to-end learning",
      "stargazers_count": 28773,
      "forks_count": 6045,
      "open_issues_count": 921,
      "pushed_at": "2026-11-09T13:37:13Z",
      "updated_at": "2026-11-09T15:35:42Z",
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      "created_at": "2024-01-26T15:50:41Z",
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      "fork": false,
      "github_id": 748713144,
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        "spdx_id": "Apache-2.0",
        "name": "Apache License 2.0",
        "url": "https://api.github.com/licenses/apache-2.0"
      },
      "latest_release": {
        "tag_name": "v0.7.0",
        "published_at": "2026-11-05T17:13:20Z",
        "html_url": "https://github.com/huggingface/lerobot/releases/tag/v0.7.0"
      },
      "observed_at": "2026-11-09T16:04:44Z",
      "source_status": "ok",
      "stars_delta_7d": 129,
      "stars_baseline_at": "2026-11-02T09:40:20Z",
      "attempted_at": "2026-11-09T16:04:43Z",
      "refresh_status": "refreshed"
    },
    {
      "id": "isaaclab",
      "name": "Isaac Lab",
      "repo": "isaac-sim/IsaacLab",
      "fields": [
        "로봇",
        "제조"
      ],
      "techniques": [
        "AI",
        "시뮬레이션"
      ],
      "tags": [
        "Python",
        "강화학습",
        "로봇 학습"
      ],
      "summary_ko": "여러 물리·렌더링 백엔드로 로봇 학습 환경과 실험을 구성하는 프레임워크.",
      "design_note": "로봇 작업의 관측·행동·보상과 가상 학습 환경을 설계할 때 검토한다.",
      "operations_note": "선택한 백엔드의 GPU·버전·의존성 조건을 확인하고 실물 전이 성능은 별도 평가한다.",
      "full_name": "isaac-sim/IsaacLab",
      "html_url": "https://github.com/isaac-sim/IsaacLab",
      "description": "Unified framework for robot learning with multi-physics/renderer support",
      "stargazers_count": 8602,
      "forks_count": 4005,
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      "pushed_at": "2026-11-09T07:37:05Z",
      "updated_at": "2026-11-09T07:37:10Z",
      "archived": false,
      "created_at": "2022-11-16T23:55:13Z",
      "language": "Python",
      "fork": false,
      "github_id": 567038244,
      "license": {
        "spdx_id": "NOASSERTION",
        "name": "Other",
        "url": null
      },
      "latest_release": {
        "tag_name": "v3.0.0",
        "published_at": "2026-10-20T02:05:51Z",
        "html_url": "https://github.com/isaac-sim/IsaacLab/releases/tag/v3.0.0"
      },
      "observed_at": "2026-11-09T09:57:28Z",
      "source_status": "ok",
      "stars_delta_7d": null,
      "stars_baseline_at": null,
      "attempted_at": "2026-11-09T09:57:28Z",
      "refresh_status": "deferred",
      "discovery_sources": [
        {
          "source_id": "openrobotics-discourse",
          "source_name": "ROS · Open Robotics Discourse",
          "source_url": "https://discourse.openrobotics.org/latest",
          "context_title": "ROS News for the Week of October 19th, 2026",
          "context_summary": "ROS News for the Week of October 19th, 2026 Right about now ROSConUK is wrapping up in Edinburgh! The photos from the event look fantastic, particularly the limited edition PR2 Red Rye Ale. Meanwhile, on the other side of the globe, a sold out ROSCon Singapore is kicking off! Later next week ROSCon Spain is happening in Valencia . ROSCon China happened last weekend and here’s a short video report of the exhibitors .…"
        }
      ]
    },
    {
      "id": "mujoco",
      "name": "MuJoCo",
      "repo": "google-deepmind/mujoco",
      "fields": [
        "로봇",
        "제조"
      ],
      "techniques": [
        "시뮬레이션",
        "AI"
      ],
      "tags": [
        "C",
        "C++",
        "접촉 동역학"
      ],
      "summary_ko": "접촉과 다물체 동역학을 계산하는 물리 시뮬레이션 엔진.",
      "design_note": "기구·관절·접촉 조건과 제어 정책의 가정을 시험하는 데 활용한다.",
      "operations_note": "실물의 마찰·구동기·센서 특성을 보정해야 하며 실행 성공이 현장 정확도를 보장하지 않는다.",
      "full_name": "google-deepmind/mujoco",
      "html_url": "https://github.com/google-deepmind/mujoco",
      "description": "Multi-Joint dynamics with Contact. A general purpose physics simulator.",
      "stargazers_count": 16672,
      "forks_count": 1966,
      "open_issues_count": 307,
      "pushed_at": "2026-11-09T08:53:06Z",
      "updated_at": "2026-11-09T09:53:52Z",
      "archived": false,
      "created_at": "2021-08-27T12:25:48Z",
      "language": "C++",
      "fork": false,
      "github_id": 400501136,
      "license": {
        "spdx_id": "Apache-2.0",
        "name": "Apache License 2.0",
        "url": "https://api.github.com/licenses/apache-2.0"
      },
      "latest_release": {
        "tag_name": "4.0.0",
        "published_at": "2026-11-06T15:39:53Z",
        "html_url": "https://github.com/google-deepmind/mujoco/releases/tag/4.0.0"
      },
      "observed_at": "2026-11-09T09:57:29Z",
      "source_status": "ok",
      "stars_delta_7d": null,
      "stars_baseline_at": null,
      "attempted_at": "2026-11-09T09:57:28Z",
      "refresh_status": "deferred",
      "discovery_sources": [
        {
          "source_id": "gazebo-discourse",
          "source_name": "Gazebo Community",
          "source_url": "https://discourse.openrobotics.org/c/gazebo/95",
          "context_title": "Gazebo PMC Meeting Minutes 2026-09-28",
          "context_summary": "Hi Gazebo Community! This week’s meeting was held on Mon, Sep 28, 2026 6:00 PM UTC and the following is a summary: PMC Business Formal Decision: Accepted REP-158: OpenUSD Conventions for Simulation Asset Interoperability in Open Source Robotics . This was a joint vote with the ROS PMC, and the final results will be announced once officially recorded by both PMCs. A formal vote will be requested to sponsor Cherie Hu…"
        }
      ]
    },
    {
      "id": "or-tools",
      "name": "OR-Tools",
      "repo": "google/or-tools",
      "fields": [
        "제조",
        "물류",
        "철도"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [
        "Python",
        "C++",
        "경로",
        "스케줄링"
      ],
      "summary_ko": "경로·스케줄·정수계획 등 조합 최적화 문제를 다루는 도구 모음.",
      "design_note": "설비·차량·작업의 용량과 제약을 정리해 여러 배정안을 비교한다.",
      "operations_note": "실제 제약과 계산 시간 제한을 명시하고 불가능한 해나 미완료 탐색 상태를 구분한다.",
      "full_name": "google/or-tools",
      "html_url": "https://github.com/google/or-tools",
      "description": "Google's Operations Research tools:",
      "stargazers_count": 14347,
      "forks_count": 2545,
      "open_issues_count": 141,
      "pushed_at": "2026-11-09T09:54:51Z",
      "updated_at": "2026-11-09T09:42:17Z",
      "archived": false,
      "created_at": "2015-02-21T01:25:35Z",
      "language": "C++",
      "fork": false,
      "github_id": 31102092,
      "license": {
        "spdx_id": "Apache-2.0",
        "name": "Apache License 2.0",
        "url": "https://api.github.com/licenses/apache-2.0"
      },
      "latest_release": {
        "tag_name": "v9.15",
        "published_at": "2026-01-12T15:38:30Z",
        "html_url": "https://github.com/google/or-tools/releases/tag/v9.15"
      },
      "observed_at": "2026-11-09T09:57:29Z",
      "source_status": "ok",
      "stars_delta_7d": null,
      "stars_baseline_at": null,
      "attempted_at": "2026-11-09T09:57:29Z",
      "refresh_status": "deferred"
    },
    {
      "id": "mesa",
      "name": "Mesa",
      "repo": "mesa/mesa",
      "fields": [
        "제조",
        "물류",
        "도로교통"
      ],
      "techniques": [
        "시뮬레이션",
        "데이터 분석"
      ],
      "tags": [
        "Python",
        "에이전트 기반 모델링",
        "ABM"
      ],
      "summary_ko": "개별 주체의 행동과 상호작용을 모델링하는 Python ABM 프레임워크.",
      "design_note": "작업자·차량 등의 행동 규칙이 전체 흐름에 미치는 영향을 탐구한다.",
      "operations_note": "행동 가정을 관측 자료와 대조하고 여러 난수 시드로 결과의 변동을 확인한다.",
      "full_name": "mesa/mesa",
      "html_url": "https://github.com/mesa/mesa",
      "description": "Mesa is an open-source Python library for agent-based modeling, ideal for simulating complex systems and exploring emergent behaviors.",
      "stargazers_count": 3951,
      "forks_count": 1367,
      "open_issues_count": 131,
      "pushed_at": "2026-11-07T21:49:23Z",
      "updated_at": "2026-11-09T05:08:47Z",
      "archived": false,
      "created_at": "2014-09-19T21:16:30Z",
      "language": "Python",
      "fork": false,
      "github_id": 24245737,
      "license": {
        "spdx_id": "Apache-2.0",
        "name": "Apache License 2.0",
        "url": "https://api.github.com/licenses/apache-2.0"
      },
      "latest_release": {
        "tag_name": "v3.5.2",
        "published_at": "2026-10-28T16:25:34Z",
        "html_url": "https://github.com/mesa/mesa/releases/tag/v3.5.2"
      },
      "observed_at": "2026-11-09T09:57:30Z",
      "source_status": "ok",
      "stars_delta_7d": null,
      "stars_baseline_at": null,
      "attempted_at": "2026-11-09T09:57:29Z",
      "refresh_status": "deferred",
      "discovery_sources": [
        {
          "source_id": "awesome-simulation",
          "source_name": "Awesome Software for Simulations",
          "source_url": "https://github.com/oskar-j/awesome-sim-software",
          "context_title": "Mesa",
          "context_summary": "General purpose · 🧰 Mesa - Mesa is an agent-based modeling framework in Python."
        }
      ]
    },
    {
      "id": "ditto",
      "name": "Eclipse Ditto",
      "repo": "eclipse-ditto/ditto",
      "fields": [
        "제조",
        "물류",
        "에너지"
      ],
      "techniques": [
        "디지털트윈"
      ],
      "tags": [
        "Java",
        "IoT",
        "상태 관리",
        "API"
      ],
      "summary_ko": "연결된 사물의 디지털 표현과 상태·접근 정책을 관리하는 프레임워크.",
      "design_note": "설비 식별자와 상태 모델, 연결·권한 구조를 설계할 때 검토한다.",
      "operations_note": "물리 예측 엔진은 아니며 메시지 연결·상태 동기화·인증을 별도로 구성해야 한다.",
      "full_name": "eclipse-ditto/ditto",
      "html_url": "https://github.com/eclipse-ditto/ditto",
      "description": "Eclipse Ditto™: Digital Twin framework of Eclipse IoT - main repository",
      "stargazers_count": 947,
      "forks_count": 296,
      "open_issues_count": 106,
      "pushed_at": "2026-11-09T08:42:13Z",
      "updated_at": "2026-11-06T13:53:55Z",
      "archived": false,
      "created_at": "2017-04-10T19:29:57Z",
      "language": "Java",
      "fork": false,
      "github_id": 87849739,
      "license": {
        "spdx_id": "EPL-2.0",
        "name": "Eclipse Public License 2.0",
        "url": "https://api.github.com/licenses/epl-2.0"
      },
      "latest_release": {
        "tag_name": "3.9.8",
        "published_at": "2026-10-08T15:18:19Z",
        "html_url": "https://github.com/eclipse-ditto/ditto/releases/tag/3.9.8"
      },
      "observed_at": "2026-11-09T09:57:30Z",
      "source_status": "ok",
      "stars_delta_7d": null,
      "stars_baseline_at": null,
      "attempted_at": "2026-11-09T09:57:30Z",
      "refresh_status": "deferred"
    },
    {
      "id": "pyomo",
      "name": "Pyomo",
      "repo": "Pyomo/pyomo",
      "fields": [
        "제조",
        "물류",
        "에너지"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [
        "Python",
        "수리 모델링",
        "외부 솔버"
      ],
      "summary_ko": "선형·정수·비선형 등의 최적화 문제를 Python으로 표현하는 모델링 도구.",
      "design_note": "설계 대안의 목적함수와 운영 제약을 분리해 수리 모델로 비교한다.",
      "operations_note": "해를 계산할 솔버의 설치·라이선스·지원 문제 유형과 종료 상태를 따로 확인한다.",
      "full_name": "Pyomo/pyomo",
      "html_url": "https://github.com/Pyomo/pyomo",
      "description": "An object-oriented algebraic modeling language in Python for structured optimization problems.",
      "stargazers_count": 2561,
      "forks_count": 604,
      "open_issues_count": 391,
      "pushed_at": "2026-11-05T17:09:01Z",
      "updated_at": "2026-11-09T04:53:39Z",
      "archived": false,
      "created_at": "2016-05-27T19:33:45Z",
      "language": "Python",
      "fork": false,
      "github_id": 59858718,
      "license": {
        "spdx_id": "NOASSERTION",
        "name": "Other",
        "url": null
      },
      "latest_release": {
        "tag_name": "6.10.1",
        "published_at": "2026-06-04T21:52:31Z",
        "html_url": "https://github.com/Pyomo/pyomo/releases/tag/6.10.1"
      },
      "observed_at": "2026-11-09T09:57:31Z",
      "source_status": "ok",
      "stars_delta_7d": null,
      "stars_baseline_at": null,
      "attempted_at": "2026-11-09T09:57:30Z",
      "refresh_status": "deferred"
    },
    {
      "id": "sumo",
      "name": "Eclipse SUMO",
      "repo": "eclipse-sumo/sumo",
      "fields": [
        "도로교통",
        "철도",
        "물류"
      ],
      "techniques": [
        "시뮬레이션",
        "데이터 분석"
      ],
      "tags": [
        "C++",
        "미시 교통",
        "다중 교통수단"
      ],
      "summary_ko": "차량과 여러 교통수단의 이동을 모델링하는 교통 시뮬레이션 도구.",
      "design_note": "네트워크·수요·신호·운행 규칙을 바꿔 교통 대안을 비교한다.",
      "operations_note": "실제 계수 자료로 수요와 주행 행태를 보정하고 제어 연동 범위를 확인한다.",
      "full_name": "eclipse-sumo/sumo",
      "html_url": "https://github.com/eclipse-sumo/sumo",
      "description": "Eclipse SUMO is an open source, highly portable, microscopic and continuous traffic simulation package designed to handle large networks. It allows for intermodal simulation including pedestrians and comes with a large set of tools for scenario creation.",
      "stargazers_count": 4270,
      "forks_count": 1787,
      "open_issues_count": 2988,
      "pushed_at": "2026-11-09T11:24:29Z",
      "updated_at": "2026-11-09T08:27:20Z",
      "archived": false,
      "created_at": "2017-10-27T20:23:17Z",
      "language": "Python",
      "fork": false,
      "github_id": 108592307,
      "license": {
        "spdx_id": "EPL-2.0",
        "name": "Eclipse Public License 2.0",
        "url": "https://api.github.com/licenses/epl-2.0"
      },
      "latest_release": null,
      "observed_at": "2026-11-09T16:04:44Z",
      "source_status": "ok",
      "stars_delta_7d": 15,
      "stars_baseline_at": "2026-11-02T09:40:25Z",
      "attempted_at": "2026-11-09T16:04:44Z",
      "refresh_status": "refreshed"
    },
    {
      "id": "opensees",
      "name": "OpenSees",
      "repo": "OpenSees/OpenSees",
      "fields": [
        "건설",
        "토목공사"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [
        "C++",
        "구조해석",
        "지진 응답"
      ],
      "summary_ko": "구조물과 지반 시스템의 비선형 응답을 모델링하는 구조해석 프레임워크.",
      "design_note": "재료·요소·경계 조건을 정의해 구조 대안과 하중 응답을 비교한다.",
      "operations_note": "실제 시설 판단에는 입력·수렴·모델 가정과 적용 기준을 전문가가 별도 검토해야 한다.",
      "full_name": "OpenSees/OpenSees",
      "html_url": "https://github.com/OpenSees/OpenSees",
      "description": "OpenSees Source Code Repository",
      "stargazers_count": 855,
      "forks_count": 840,
      "open_issues_count": 68,
      "pushed_at": "2026-11-06T01:58:21Z",
      "updated_at": "2026-11-08T14:34:40Z",
      "archived": false,
      "created_at": "2018-09-12T00:49:34Z",
      "language": "C++",
      "fork": false,
      "github_id": 148399207,
      "license": {
        "spdx_id": "NOASSERTION",
        "name": "Other",
        "url": null
      },
      "latest_release": {
        "tag_name": "v3.8.0",
        "published_at": "2026-02-18T16:49:55Z",
        "html_url": "https://github.com/OpenSees/OpenSees/releases/tag/v3.8.0"
      },
      "observed_at": "2026-11-09T16:04:45Z",
      "source_status": "ok",
      "stars_delta_7d": 6,
      "stars_baseline_at": "2026-11-02T09:40:25Z",
      "attempted_at": "2026-11-09T16:04:44Z",
      "refresh_status": "refreshed"
    },
    {
      "id": "ifcopenshell",
      "name": "IfcOpenShell",
      "repo": "IfcOpenShell/IfcOpenShell",
      "fields": [
        "건설",
        "토목공사"
      ],
      "techniques": [
        "디지털트윈",
        "데이터 분석"
      ],
      "tags": [
        "C++",
        "Python",
        "IFC",
        "BIM"
      ],
      "summary_ko": "IFC 기반 건설 모델의 기하와 속성 데이터를 읽고 처리하는 도구 모음.",
      "design_note": "BIM 데이터를 해석·시뮬레이션 입력으로 변환할 때 형상과 속성 매핑을 검토한다.",
      "operations_note": "시설 데이터 연계의 재료이며 단독 운영 디지털트윈이 아니다. IFC 버전과 데이터 품질을 확인한다.",
      "full_name": "IfcOpenShell/IfcOpenShell",
      "html_url": "https://github.com/IfcOpenShell/IfcOpenShell",
      "description": "Open source IFC library and geometry engine",
      "stargazers_count": 2943,
      "forks_count": 1008,
      "open_issues_count": 2122,
      "pushed_at": "2026-11-09T15:30:37Z",
      "updated_at": "2026-11-09T10:03:43Z",
      "archived": false,
      "created_at": "2015-08-10T09:03:14Z",
      "language": "C++",
      "fork": false,
      "github_id": 40473201,
      "license": {
        "spdx_id": "LGPL-3.0",
        "name": "GNU Lesser General Public License v3.0",
        "url": "https://api.github.com/licenses/lgpl-3.0"
      },
      "latest_release": {
        "tag_name": "bonsai-0.9.1-alpha2611051029",
        "published_at": "2026-11-05T10:29:18Z",
        "html_url": "https://github.com/IfcOpenShell/IfcOpenShell/releases/tag/bonsai-0.9.1-alpha2611051029"
      },
      "observed_at": "2026-11-09T16:04:46Z",
      "source_status": "ok",
      "stars_delta_7d": 27,
      "stars_baseline_at": "2026-11-02T09:40:26Z",
      "attempted_at": "2026-11-09T16:04:45Z",
      "refresh_status": "refreshed"
    },
    {
      "id": "pypsa",
      "name": "PyPSA",
      "repo": "PyPSA/PyPSA",
      "fields": [
        "에너지"
      ],
      "techniques": [
        "최적화",
        "시뮬레이션",
        "데이터 분석"
      ],
      "tags": [
        "Python",
        "전력망",
        "에너지 시스템"
      ],
      "summary_ko": "전력·에너지 시스템의 네트워크 분석과 운영·투자 최적화를 지원하는 도구.",
      "design_note": "발전·저장·송전 용량과 에너지 시나리오의 비용·제약을 비교한다.",
      "operations_note": "시간 해상도와 수요·가격 가정을 명시하고 실제 급전 의사결정 전 모델 범위를 검증한다.",
      "full_name": "PyPSA/PyPSA",
      "html_url": "https://github.com/PyPSA/PyPSA",
      "description": "PyPSA: Python for Power System Analysis",
      "stargazers_count": 2235,
      "forks_count": 732,
      "open_issues_count": 172,
      "pushed_at": "2026-11-09T12:54:07Z",
      "updated_at": "2026-11-08T13:56:51Z",
      "archived": false,
      "created_at": "2016-01-11T09:04:18Z",
      "language": "Python",
      "fork": false,
      "github_id": 49414256,
      "license": {
        "spdx_id": "MIT",
        "name": "MIT License",
        "url": "https://api.github.com/licenses/mit"
      },
      "latest_release": {
        "tag_name": "v1.4.0",
        "published_at": "2026-10-09T12:28:26Z",
        "html_url": "https://github.com/PyPSA/PyPSA/releases/tag/v1.4.0"
      },
      "observed_at": "2026-11-09T16:04:46Z",
      "source_status": "ok",
      "stars_delta_7d": 7,
      "stars_baseline_at": "2026-11-02T09:40:27Z",
      "attempted_at": "2026-11-09T16:04:46Z",
      "refresh_status": "refreshed"
    },
    {
      "id": "basilisk",
      "name": "Basilisk",
      "repo": "AVSLab/basilisk",
      "fields": [
        "우주"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [
        "C++",
        "Python",
        "우주 비행 동역학",
        "제어"
      ],
      "summary_ko": "우주선 동역학과 비행 소프트웨어 알고리즘을 연결하는 시뮬레이션 프레임워크.",
      "design_note": "우주선의 동역학·센서·제어 모듈을 조합해 임무 가정을 시험한다.",
      "operations_note": "비행 적용을 인증하는 도구는 아니며 임무별 모델·오차·시험 조건을 따로 검증한다.",
      "full_name": "AVSLab/basilisk",
      "html_url": "https://github.com/AVSLab/basilisk",
      "description": "Astrodynamics simulation framework",
      "stargazers_count": 419,
      "forks_count": 147,
      "open_issues_count": 11,
      "pushed_at": "2026-11-09T15:23:50Z",
      "updated_at": "2026-11-08T11:44:06Z",
      "archived": false,
      "created_at": "2022-12-13T01:59:21Z",
      "language": "C",
      "fork": false,
      "github_id": 577555866,
      "license": {
        "spdx_id": "ISC",
        "name": "ISC License",
        "url": "https://api.github.com/licenses/isc"
      },
      "latest_release": {
        "tag_name": "v2.12.2",
        "published_at": "2026-11-01T23:29:40Z",
        "html_url": "https://github.com/AVSLab/basilisk/releases/tag/v2.12.2"
      },
      "observed_at": "2026-11-09T16:04:47Z",
      "source_status": "ok",
      "stars_delta_7d": 2,
      "stars_baseline_at": "2026-11-02T09:40:27Z",
      "attempted_at": "2026-11-09T16:04:46Z",
      "refresh_status": "refreshed"
    },
    {
      "id": "simdissdk",
      "name": "SIMDIS SDK",
      "repo": "USNavalResearchLaboratory/simdissdk",
      "fields": [
        "국방"
      ],
      "techniques": [
        "시뮬레이션",
        "데이터 분석"
      ],
      "tags": [
        "C++",
        "지리공간",
        "3D 시각화",
        "결과 분석"
      ],
      "summary_ko": "NRL이 공개한 시간별 객체 상태의 지도 기반 3D 표시·분석 SDK.",
      "design_note": "시간에 따라 변하는 객체 위치와 상태를 시각화하는 분석 도구의 기반으로 검토한다.",
      "operations_note": "SIMDIS 완제품이나 전투 시뮬레이터와 구분하며 자료·의존성·배포 조건을 각각 확인한다.",
      "full_name": "USNavalResearchLaboratory/simdissdk",
      "html_url": "https://github.com/USNavalResearchLaboratory/simdissdk",
      "description": "SIMDIS SDK",
      "stargazers_count": 146,
      "forks_count": 54,
      "open_issues_count": 0,
      "pushed_at": "2026-11-06T12:14:44Z",
      "updated_at": "2026-11-06T12:14:49Z",
      "archived": false,
      "created_at": "2017-08-22T11:10:12Z",
      "language": "C++",
      "fork": false,
      "github_id": 101054357,
      "license": {
        "spdx_id": "NOASSERTION",
        "name": "Other",
        "url": null
      },
      "latest_release": {
        "tag_name": "simdissdk-1.25",
        "published_at": "2026-09-11T20:17:21Z",
        "html_url": "https://github.com/USNavalResearchLaboratory/simdissdk/releases/tag/simdissdk-1.25"
      },
      "observed_at": "2026-11-09T16:04:47Z",
      "source_status": "ok",
      "stars_delta_7d": 0,
      "stars_baseline_at": "2026-11-02T09:40:28Z",
      "attempted_at": "2026-11-09T16:04:47Z",
      "refresh_status": "refreshed"
    },
    {
      "id": "ros2",
      "name": "ROS 2",
      "repo": "ros2/ros2",
      "fields": [
        "로봇",
        "제조",
        "물류"
      ],
      "techniques": [
        "시뮬레이션",
        "디지털트윈"
      ],
      "tags": [
        "로봇 미들웨어",
        "통신",
        "메타 저장소",
        "실물 연계"
      ],
      "summary_ko": "로봇 소프트웨어의 통신과 실행 구성을 위한 ROS 2 메타 저장소.",
      "design_note": "실물 로봇과 가상 환경이 공유할 노드·메시지·연결 구조를 설계한다.",
      "operations_note": "물리 시뮬레이터는 아니며 배포판·드라이버·QoS·실시간 요구를 별도로 맞춰야 한다.",
      "full_name": "ros2/ros2",
      "html_url": "https://github.com/ros2/ros2",
      "description": "The Robot Operating System, is a meta operating system for robots.",
      "stargazers_count": 6296,
      "forks_count": 981,
      "open_issues_count": 141,
      "pushed_at": "2026-11-04T14:02:39Z",
      "updated_at": "2026-11-09T09:24:53Z",
      "archived": false,
      "created_at": "2015-02-24T22:46:55Z",
      "language": null,
      "fork": false,
      "github_id": 31285942,
      "license": null,
      "latest_release": {
        "tag_name": "release-lyrical-20260807",
        "published_at": "2026-08-08T00:05:41Z",
        "html_url": "https://github.com/ros2/ros2/releases/tag/release-lyrical-20260807"
      },
      "observed_at": "2026-11-09T09:57:31Z",
      "source_status": "ok",
      "stars_delta_7d": null,
      "stars_baseline_at": null,
      "attempted_at": "2026-11-09T09:57:31Z",
      "refresh_status": "deferred",
      "discovery_sources": [
        {
          "source_id": "awesome-robotics",
          "source_name": "Awesome Robotics Libraries",
          "source_url": "https://github.com/jslee02/awesome-robotics-libraries",
          "context_title": "ROS 2",
          "context_summary": "Middleware & Frameworks · 🟢 ROS 2 - Version 2.0 of the Robot Operating System (ROS) software stack. ⭐ 6.2k"
        },
        {
          "source_id": "openrobotics-discourse",
          "source_name": "ROS · Open Robotics Discourse",
          "source_url": "https://discourse.openrobotics.org/latest",
          "context_title": "ROS PMC Minutes for October 20, 2026",
          "context_summary": "The ROS PMC meeting for this week was on Tuesday, October 20, 2026. The notes from the meeting are available at ROS PMC weekly meeting agenda . ROS PMC Business [@ahcorde ] Votes are going out to the PMC members for: @skye.galaxy as the ROS representative to the TGC 3 new committers @christophebedard as ROSboss for M-turtle @clalancette advised checking the voting requirements against the charter , so the process is…"
        },
        {
          "source_id": "openrobotics-discourse",
          "source_name": "ROS · Open Robotics Discourse",
          "source_url": "https://discourse.openrobotics.org/latest",
          "context_title": "Topic statistics and libstatistics_collector removed from rclcpp in Rolling (Makoa Mata-mata)",
          "context_summary": "Hi everyone, Heads-up about an API removal that recently landed in Rolling and will be part of ROS 2 Makoa Mata-mata (May 23, 2027): built-in topic statistics have been removed from rclcpp , and libstatistics_collector is no longer part of the ROS 2 core. What changed The following PRs were merged on October 5, 2026: ros2/rclcpp#3203 : removes the topic statistics implementation from rclcpp , rclcpp_lifecycle and rc…"
        }
      ]
    },
    {
      "id": "bullet3",
      "name": "Bullet Physics",
      "repo": "bulletphysics/bullet3",
      "fields": [
        "로봇",
        "제조"
      ],
      "techniques": [
        "시뮬레이션",
        "AI"
      ],
      "tags": [
        "C++",
        "PyBullet",
        "물리 엔진",
        "충돌"
      ],
      "summary_ko": "충돌·강체 동역학과 로봇 학습 실험에 활용할 수 있는 물리 엔진.",
      "design_note": "로봇 기구와 접촉 조건, 제어 알고리즘의 가상 실험을 구성한다.",
      "operations_note": "시간 간격·접촉 파라미터와 모델 형식에 따라 결과가 달라져 대상 시스템과 대조해야 한다.",
      "full_name": "bulletphysics/bullet3",
      "html_url": "https://github.com/bulletphysics/bullet3",
      "description": "Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc.",
      "stargazers_count": 14845,
      "forks_count": 3113,
      "open_issues_count": 444,
      "pushed_at": "2026-10-09T21:51:41Z",
      "updated_at": "2026-11-09T07:51:43Z",
      "archived": false,
      "created_at": "2011-04-12T18:45:08Z",
      "language": "C++",
      "fork": false,
      "github_id": 1605598,
      "license": {
        "spdx_id": "NOASSERTION",
        "name": "Other",
        "url": null
      },
      "latest_release": {
        "tag_name": "3.25",
        "published_at": "2022-04-24T23:54:27Z",
        "html_url": "https://github.com/bulletphysics/bullet3/releases/tag/3.25"
      },
      "observed_at": "2026-11-09T09:57:32Z",
      "source_status": "ok",
      "stars_delta_7d": null,
      "stars_baseline_at": null,
      "attempted_at": "2026-11-09T09:57:31Z",
      "refresh_status": "deferred"
    },
    {
      "id": "gz-sim",
      "name": "Gazebo Sim",
      "repo": "gazebosim/gz-sim",
      "fields": [
        "로봇",
        "제조",
        "물류"
      ],
      "techniques": [
        "시뮬레이션",
        "디지털트윈"
      ],
      "tags": [
        "C++",
        "로봇 환경",
        "센서",
        "플러그인"
      ],
      "summary_ko": "로봇·환경·센서와 물리 동작을 구성하는 Gazebo 시뮬레이션 구성요소.",
      "design_note": "설비 배치와 로봇 센서·제어가 만나는 가상 작업 환경을 설계한다.",
      "operations_note": "Gazebo와 ROS 배포판의 호환성, 사용 플러그인과 실제 센서 오차를 확인한다.",
      "full_name": "gazebosim/gz-sim",
      "html_url": "https://github.com/gazebosim/gz-sim",
      "description": "Open source robotics simulator. The latest version of Gazebo.",
      "stargazers_count": 1570,
      "forks_count": 520,
      "open_issues_count": 673,
      "pushed_at": "2026-11-08T21:11:49Z",
      "updated_at": "2026-11-08T21:11:55Z",
      "archived": false,
      "created_at": "2020-04-15T09:17:50Z",
      "language": "C++",
      "fork": false,
      "github_id": 255865265,
      "license": {
        "spdx_id": "Apache-2.0",
        "name": "Apache License 2.0",
        "url": "https://api.github.com/licenses/apache-2.0"
      },
      "latest_release": {
        "tag_name": "gz-sim10_10.0.0",
        "published_at": "2025-10-14T04:14:45Z",
        "html_url": "https://github.com/gazebosim/gz-sim/releases/tag/gz-sim10_10.0.0"
      },
      "observed_at": "2026-11-09T09:57:32Z",
      "source_status": "ok",
      "stars_delta_7d": null,
      "stars_baseline_at": null,
      "attempted_at": "2026-11-09T09:57:32Z",
      "refresh_status": "deferred",
      "discovery_sources": [
        {
          "source_id": "openrobotics-discourse",
          "source_name": "ROS · Open Robotics Discourse",
          "source_url": "https://discourse.openrobotics.org/latest",
          "context_title": "Gazebo PMC Meeting Minutes 2026-10-12",
          "context_summary": "Hi Gazebo Community! This week’s meeting was held on Mon, Oct 12, 2026 6:00 PM UTC and the following is a summary: PMC Business Approved Cherrie Lu for mentee committer status. Voting is currently open via email on adding Luca Della Vedova as a mentee PMC member. New agenda items Gazebo Maintainer Audit & Rebalancing (Phase 2) : A reminder that Phase 2 of the Gazebo Maintainer Audit & Rebalancing Workspace (2026) (n…"
        },
        {
          "source_id": "gazebo-discourse",
          "source_name": "Gazebo Community",
          "source_url": "https://discourse.openrobotics.org/c/gazebo/95",
          "context_title": "Gazebo PMC Meeting Minutes 2026-10-12",
          "context_summary": "Hi Gazebo Community! This week’s meeting was held on Mon, Oct 12, 2026 6:00 PM UTC and the following is a summary: PMC Business Approved Cherrie Lu for mentee committer status. Voting is currently open via email on adding Luca Della Vedova as a mentee PMC member. New agenda items Gazebo Maintainer Audit & Rebalancing (Phase 2) : A reminder that Phase 2 of the Gazebo Maintainer Audit & Rebalancing Workspace (2026) (n…"
        },
        {
          "source_id": "gazebo-discourse",
          "source_name": "Gazebo Community",
          "source_url": "https://discourse.openrobotics.org/c/gazebo/95",
          "context_title": "Gazebo PMC Meeting Minutes 2026-10-05",
          "context_summary": "Hi Gazebo Community! This week’s meeting was held on Mon, Oct 5, 2026 6:00 PM UTC and the following is a summary: PMC Business PMC Membership & Mentee Committer Voting : Voting is currently open for adding Cherry as a mentee committer, and an asynchronous email vote is being opened for Luca Della Vedova’s nomination to the PMC. Asynchronous Meeting Engagement : The PMC agreed to encourage members and contributors in…"
        },
        {
          "source_id": "gazebo-discourse",
          "source_name": "Gazebo Community",
          "source_url": "https://discourse.openrobotics.org/c/gazebo/95",
          "context_title": "Gazebo PMC Meeting Minutes 2026-09-28",
          "context_summary": "Hi Gazebo Community! This week’s meeting was held on Mon, Sep 28, 2026 6:00 PM UTC and the following is a summary: PMC Business Formal Decision: Accepted REP-158: OpenUSD Conventions for Simulation Asset Interoperability in Open Source Robotics . This was a joint vote with the ROS PMC, and the final results will be announced once officially recorded by both PMCs. A formal vote will be requested to sponsor Cherie Hu…"
        },
        {
          "source_id": "openrobotics-discourse",
          "source_name": "ROS · Open Robotics Discourse",
          "source_url": "https://discourse.openrobotics.org/latest",
          "context_title": "Gazebo PMC Meeting Minutes 2026-10-19",
          "context_summary": "Hi Gazebo Community! This week’s meeting was held on Mon, Oct 19, 2026 6:00 PM UTC and the following is a summary: PMC Business Approved adding Luca Della Vedova as a mentee PMC member. New agenda items Community Meeting : A reminder for this week’s community meeting on Scalable Multi-Robot Simulation in ROS 2 and Gazebo , featuring multi-robot simulation, namespaces, and automatic bridging. Please note the change i…"
        },
        {
          "source_id": "gazebo-discourse",
          "source_name": "Gazebo Community",
          "source_url": "https://discourse.openrobotics.org/c/gazebo/95",
          "context_title": "Gazebo PMC Meeting Minutes 2026-10-19",
          "context_summary": "Hi Gazebo Community! This week’s meeting was held on Mon, Oct 19, 2026 6:00 PM UTC and the following is a summary: PMC Business Approved adding Luca Della Vedova as a mentee PMC member. New agenda items Community Meeting : A reminder for this week’s community meeting on Scalable Multi-Robot Simulation in ROS 2 and Gazebo , featuring multi-robot simulation, namespaces, and automatic bridging. Please note the change i…"
        },
        {
          "source_id": "openrobotics-discourse",
          "source_name": "ROS · Open Robotics Discourse",
          "source_url": "https://discourse.openrobotics.org/latest",
          "context_title": "Gazebo PMC Meeting Minutes 2026-10-26",
          "context_summary": "Hi Gazebo Community! This week’s meeting was held on Mon, Oct 26, 2026 6:00 PM UTC and the following is a summary: PMC Business None this week. New agenda items Buildfarm stormed by contributions : A nice problem to have! We discussed whether we want or not to trigger CI automatically on external contributions. In the short term, new buildfarm nodes were added manually. Some ideas discussed: Should maintainers have…"
        },
        {
          "source_id": "gazebo-discourse",
          "source_name": "Gazebo Community",
          "source_url": "https://discourse.openrobotics.org/c/gazebo/95",
          "context_title": "Gazebo PMC Meeting Minutes 2026-10-26",
          "context_summary": "Hi Gazebo Community! This week’s meeting was held on Mon, Oct 26, 2026 6:00 PM UTC and the following is a summary: PMC Business None this week. New agenda items Buildfarm stormed by contributions : A nice problem to have! We discussed whether we want or not to trigger CI automatically on external contributions. In the short term, new buildfarm nodes were added manually. Some ideas discussed: Should maintainers have…"
        },
        {
          "source_id": "openrobotics-discourse",
          "source_name": "ROS · Open Robotics Discourse",
          "source_url": "https://discourse.openrobotics.org/latest",
          "context_title": "[GSoC 2026] Scalable Multi-Robot Simulation in ROS 2 and Gazebo",
          "context_summary": "Organization: Open Robotics Contributor: Jiayi Cai (@C88-YQ ) Mentor: Addisu Taddese (@azeey ) Community Meeting Talk : Talk video ; Talk slides Project Overview Multi-robot simulation is widely used in robotics research and development, including warehouse automation, robot swarms, and multi-robot coordination. However, setting up a multi-robot simulation in ROS 2 and Gazebo can require a lot of repetitive configur…"
        },
        {
          "source_id": "openrobotics-discourse",
          "source_name": "ROS · Open Robotics Discourse",
          "source_url": "https://discourse.openrobotics.org/latest",
          "context_title": "Gazebo PMC Meeting Minutes 2026-11-02",
          "context_summary": "Hi Gazebo Community! This week’s meeting was held on Mon, Nov 2, 2026 7:00 PM UTC and the following is a summary: PMC Business Following the end of Daylight Saving Time in the US, the PMC is voting asynchronously to move the weekly meeting time to 19:00 UTC to preserve the existing local meeting time. Because the OSRA TGC charter strictly requires meetings to be scheduled in UTC, a formal vote is held each time Dayl…"
        },
        {
          "source_id": "gazebo-discourse",
          "source_name": "Gazebo Community",
          "source_url": "https://discourse.openrobotics.org/c/gazebo/95",
          "context_title": "Gazebo PMC Meeting Minutes 2026-11-02",
          "context_summary": "Hi Gazebo Community! This week’s meeting was held on Mon, Nov 2, 2026 7:00 PM UTC and the following is a summary: PMC Business Following the end of Daylight Saving Time in the US, the PMC is voting asynchronously to move the weekly meeting time to 19:00 UTC to preserve the existing local meeting time. Because the OSRA TGC charter strictly requires meetings to be scheduled in UTC, a formal vote is held each time Dayl…"
        }
      ]
    },
    {
      "id": "webots",
      "name": "Webots",
      "repo": "cyberbotics/webots",
      "fields": [
        "로봇",
        "도로교통"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [
        "C++",
        "로봇 시뮬레이터",
        "센서",
        "제어기"
      ],
      "summary_ko": "로봇 모델·센서·제어기를 조합해 가상 환경에서 시험하는 시뮬레이터.",
      "design_note": "모바일 로봇과 자율주행 실험의 환경·센서·제어 구성을 비교한다.",
      "operations_note": "지원 로봇·제어 인터페이스와 시뮬레이션 시간 설정을 확인한 뒤 실물 결과와 대조한다.",
      "full_name": "cyberbotics/webots",
      "html_url": "https://github.com/cyberbotics/webots",
      "description": "Webots Robot Simulator",
      "stargazers_count": 4761,
      "forks_count": 2097,
      "open_issues_count": 231,
      "pushed_at": "2026-11-09T06:18:06Z",
      "updated_at": "2026-11-09T06:16:34Z",
      "archived": false,
      "created_at": "2018-11-05T14:09:10Z",
      "language": "C++",
      "fork": false,
      "github_id": 156228018,
      "license": {
        "spdx_id": "Apache-2.0",
        "name": "Apache License 2.0",
        "url": "https://api.github.com/licenses/apache-2.0"
      },
      "latest_release": {
        "tag_name": "R2025a",
        "published_at": "2025-02-04T12:36:33Z",
        "html_url": "https://github.com/cyberbotics/webots/releases/tag/R2025a"
      },
      "observed_at": "2026-11-09T09:57:32Z",
      "source_status": "ok",
      "stars_delta_7d": null,
      "stars_baseline_at": null,
      "attempted_at": "2026-11-09T09:57:32Z",
      "refresh_status": "deferred"
    },
    {
      "id": "pandapower",
      "name": "pandapower",
      "repo": "e2nIEE/pandapower",
      "fields": [
        "에너지"
      ],
      "techniques": [
        "시뮬레이션",
        "최적화",
        "데이터 분석"
      ],
      "tags": [
        "Python",
        "전력 조류",
        "계통 분석",
        "상태 추정"
      ],
      "summary_ko": "전력망 모델링과 조류·계통 분석 및 최적화를 지원하는 Python 도구.",
      "design_note": "망 구성·설비·부하 조건에 따른 전압과 전력 흐름을 비교한다.",
      "operations_note": "실제 계통 데이터와 단위·전압 기준을 점검하고 선택한 해석 솔버의 범위를 확인한다.",
      "full_name": "e2nIEE/pandapower",
      "html_url": "https://github.com/e2nIEE/pandapower",
      "description": "Convenient Power System Modelling and Analysis based on PYPOWER and pandas",
      "stargazers_count": 1320,
      "forks_count": 625,
      "open_issues_count": 166,
      "pushed_at": "2026-11-09T09:49:31Z",
      "updated_at": "2026-11-09T09:33:34Z",
      "archived": false,
      "created_at": "2017-01-12T13:27:53Z",
      "language": "Python",
      "fork": false,
      "github_id": 78748060,
      "license": {
        "spdx_id": "NOASSERTION",
        "name": "Other",
        "url": null
      },
      "latest_release": {
        "tag_name": "v3.5.6",
        "published_at": "2026-10-07T10:50:18Z",
        "html_url": "https://github.com/e2nIEE/pandapower/releases/tag/v3.5.6"
      },
      "observed_at": "2026-11-09T09:57:33Z",
      "source_status": "ok",
      "stars_delta_7d": null,
      "stars_baseline_at": null,
      "attempted_at": "2026-11-09T09:57:32Z",
      "refresh_status": "deferred"
    },
    {
      "id": "energyplus",
      "name": "EnergyPlus",
      "repo": "NatLabRockies/EnergyPlus",
      "fields": [
        "건설",
        "에너지"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [
        "C++",
        "건물 에너지",
        "공조",
        "물 사용"
      ],
      "summary_ko": "건물의 에너지 소비와 물 사용을 모델링하는 시뮬레이션 프로그램.",
      "design_note": "외피·공조·운영 일정과 기후 조건을 바꾸어 건물 설계 대안을 비교한다.",
      "operations_note": "실측 소비와 입력 가정을 보정하고 모델 입력 형식 및 정식 릴리스 호환성을 확인한다.",
      "full_name": "NatLabRockies/EnergyPlus",
      "html_url": "https://github.com/NatLabRockies/EnergyPlus",
      "description": "EnergyPlus™ is a whole building energy simulation program that engineers, architects, and researchers use to model both energy consumption and water use in buildings.",
      "stargazers_count": 1620,
      "forks_count": 501,
      "open_issues_count": 881,
      "pushed_at": "2026-11-09T02:48:11Z",
      "updated_at": "2026-11-09T09:35:02Z",
      "archived": false,
      "created_at": "2013-11-22T14:47:34Z",
      "language": "C++",
      "fork": false,
      "github_id": 14620185,
      "license": {
        "spdx_id": "NOASSERTION",
        "name": "Other",
        "url": null
      },
      "latest_release": {
        "tag_name": "v26.2.0",
        "published_at": "2026-09-30T13:54:03Z",
        "html_url": "https://github.com/NatLabRockies/EnergyPlus/releases/tag/v26.2.0"
      },
      "observed_at": "2026-11-09T09:57:33Z",
      "source_status": "ok",
      "stars_delta_7d": null,
      "stars_baseline_at": null,
      "attempted_at": "2026-11-09T09:57:33Z",
      "refresh_status": "deferred"
    },
    {
      "id": "modflow6",
      "name": "MODFLOW 6",
      "repo": "MODFLOW-ORG/modflow6",
      "fields": [
        "토목공사"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [
        "Fortran",
        "지하수",
        "수치 모델",
        "환경"
      ],
      "summary_ko": "지하수 흐름과 관련 수송 문제를 다루는 USGS의 수치 모델링 프로그램.",
      "design_note": "토목 현장의 지하수 경계·대수층·배수 조건에 대한 모델을 검토한다.",
      "operations_note": "현장 지질·관측수위·경계 자료가 필요하며 격자·보정·불확실성을 별도로 검토한다.",
      "full_name": "MODFLOW-ORG/modflow6",
      "html_url": "https://github.com/MODFLOW-ORG/modflow6",
      "description": "USGS Modular Hydrologic Model",
      "stargazers_count": 387,
      "forks_count": 170,
      "open_issues_count": 113,
      "pushed_at": "2026-11-05T14:28:54Z",
      "updated_at": "2026-11-05T14:29:00Z",
      "archived": false,
      "created_at": "2018-01-03T15:00:55Z",
      "language": "Fortran",
      "fork": false,
      "github_id": 116149490,
      "license": {
        "spdx_id": "NOASSERTION",
        "name": "Other",
        "url": null
      },
      "latest_release": {
        "tag_name": "6.8.2",
        "published_at": "2026-10-30T21:36:58Z",
        "html_url": "https://github.com/MODFLOW-ORG/modflow6/releases/tag/6.8.2"
      },
      "observed_at": "2026-11-09T16:04:48Z",
      "source_status": "ok",
      "stars_delta_7d": 1,
      "stars_baseline_at": "2026-11-02T09:40:29Z",
      "attempted_at": "2026-11-09T16:04:47Z",
      "refresh_status": "refreshed"
    },
    {
      "id": "osrd",
      "name": "OSRD",
      "repo": "OpenRailAssociation/osrd",
      "fields": [
        "철도"
      ],
      "techniques": [
        "시뮬레이션",
        "최적화"
      ],
      "tags": [
        "철도 인프라",
        "열차 운행",
        "시간표",
        "용량 분석"
      ],
      "summary_ko": "철도 인프라 설계·용량 분석·시간표와 운행 시뮬레이션을 위한 웹 애플리케이션.",
      "design_note": "인프라와 차량 조건을 바꾸어 운행 시간표와 수용 용량을 비교한다.",
      "operations_note": "대상 노선의 신호·차량·운행 규칙과 데이터 정확도를 확인한 뒤 결과를 해석한다.",
      "full_name": "OpenRailAssociation/osrd",
      "html_url": "https://github.com/OpenRailAssociation/osrd",
      "description": "An open source web application for railway infrastructure design, capacity analysis, timetabling and simulation",
      "stargazers_count": 675,
      "forks_count": 99,
      "open_issues_count": 886,
      "pushed_at": "2026-11-09T16:04:26Z",
      "updated_at": "2026-11-09T15:59:57Z",
      "archived": false,
      "created_at": "2019-08-21T15:21:27Z",
      "language": "TypeScript",
      "fork": false,
      "github_id": 203611792,
      "license": null,
      "latest_release": {
        "tag_name": "v1.3.3",
        "published_at": "2026-10-30T09:06:51Z",
        "html_url": "https://github.com/OpenRailAssociation/osrd/releases/tag/v1.3.3"
      },
      "observed_at": "2026-11-09T16:04:49Z",
      "source_status": "ok",
      "stars_delta_7d": 5,
      "stars_baseline_at": "2026-11-02T09:40:29Z",
      "attempted_at": "2026-11-09T16:04:48Z",
      "refresh_status": "refreshed"
    },
    {
      "id": "flatland-rl",
      "name": "Flatland",
      "repo": "flatland-association/flatland-rl",
      "fields": [
        "철도",
        "물류"
      ],
      "techniques": [
        "AI",
        "시뮬레이션",
        "최적화"
      ],
      "tags": [
        "Python",
        "다중 에이전트",
        "자원 배분",
        "강화학습"
      ],
      "summary_ko": "철도 같은 자원 배분 문제의 계획·학습 정책을 실험하는 다중 에이전트 환경.",
      "design_note": "노선과 자원 충돌을 단순화한 환경에서 배정·제어 알고리즘을 비교한다.",
      "operations_note": "연구 환경의 추상화 수준을 실제 철도 규칙과 구분하며 실무 운행 적합성을 단정하지 않는다.",
      "full_name": "flatland-association/flatland-rl",
      "html_url": "https://github.com/flatland-association/flatland-rl",
      "description": "The Flatland Framework is a multi-purpose environment to tackle problems around resilient resource allocation under uncertainty. It is designed to be a flexible and method agnostic to solve a wide range of problems in the field of operations research and reinforcement learning.",
      "stargazers_count": 74,
      "forks_count": 22,
      "open_issues_count": 63,
      "pushed_at": "2026-11-06T13:36:26Z",
      "updated_at": "2026-11-06T13:34:02Z",
      "archived": false,
      "created_at": "2023-04-02T13:31:16Z",
      "language": "Jupyter Notebook",
      "fork": false,
      "github_id": 622559800,
      "license": {
        "spdx_id": "MIT",
        "name": "MIT License",
        "url": "https://api.github.com/licenses/mit"
      },
      "latest_release": {
        "tag_name": "v4.3.0",
        "published_at": "2026-08-10T11:52:11Z",
        "html_url": "https://github.com/flatland-association/flatland-rl/releases/tag/v4.3.0"
      },
      "observed_at": "2026-11-09T16:04:49Z",
      "source_status": "ok",
      "stars_delta_7d": 0,
      "stars_baseline_at": "2026-11-02T09:40:30Z",
      "attempted_at": "2026-11-09T16:04:49Z",
      "refresh_status": "refreshed"
    },
    {
      "id": "open3d",
      "name": "Open3D",
      "repo": "isl-org/Open3D",
      "fields": [
        "로봇",
        "건설",
        "토목공사"
      ],
      "techniques": [
        "데이터 분석",
        "디지털트윈"
      ],
      "tags": [
        "C++",
        "Python",
        "점군",
        "3D 재구성"
      ],
      "summary_ko": "점군·메시 등 3D 데이터를 처리하고 재구성하는 도구 모음.",
      "design_note": "현장 스캔을 가상 모델의 형상 입력으로 연결하는 처리 과정을 검토한다.",
      "operations_note": "단독 운영 트윈이나 물리 엔진은 아니며 좌표계·정합 오차·센서 품질을 관리해야 한다.",
      "full_name": "isl-org/Open3D",
      "html_url": "https://github.com/isl-org/Open3D",
      "description": "Open3D: A Modern Library for 3D Data Processing",
      "stargazers_count": 14187,
      "forks_count": 2650,
      "open_issues_count": 1373,
      "pushed_at": "2026-11-05T18:33:08Z",
      "updated_at": "2026-11-09T09:47:19Z",
      "archived": false,
      "created_at": "2016-12-02T16:40:38Z",
      "language": "C++",
      "fork": false,
      "github_id": 75413130,
      "license": {
        "spdx_id": "NOASSERTION",
        "name": "Other",
        "url": null
      },
      "latest_release": {
        "tag_name": "v0.20.0",
        "published_at": "2026-09-16T21:38:20Z",
        "html_url": "https://github.com/isl-org/Open3D/releases/tag/v0.20.0"
      },
      "observed_at": "2026-11-09T09:57:34Z",
      "source_status": "ok",
      "stars_delta_7d": null,
      "stars_baseline_at": null,
      "attempted_at": "2026-11-09T09:57:33Z",
      "refresh_status": "deferred"
    },
    {
      "id": "tudatpy",
      "name": "TudatPy",
      "repo": "tudat-team/tudatpy",
      "fields": [
        "우주"
      ],
      "techniques": [
        "시뮬레이션",
        "데이터 분석"
      ],
      "tags": [
        "Python",
        "천체 동역학",
        "궤도 전파",
        "추정"
      ],
      "summary_ko": "천체 동역학과 우주 임무의 전파·추정 실험을 위한 Tudat의 Python 인터페이스.",
      "design_note": "힘 모델과 관측 가정을 구성해 궤도 및 임무 분석 대안을 비교한다.",
      "operations_note": "천체력·시간계·좌표계와 수치 적분 설정을 명시하고 기준 결과와 대조한다.",
      "full_name": "tudat-team/tudatpy",
      "html_url": "https://github.com/tudat-team/tudatpy",
      "description": "A Python platform to perform astrodynamics and space research.",
      "stargazers_count": 102,
      "forks_count": 57,
      "open_issues_count": 138,
      "pushed_at": "2026-11-09T16:02:44Z",
      "updated_at": "2026-11-09T15:11:38Z",
      "archived": false,
      "created_at": "2018-12-10T18:25:40Z",
      "language": "C++",
      "fork": false,
      "github_id": 161218433,
      "license": {
        "spdx_id": "BSD-3-Clause",
        "name": "BSD 3-Clause \"New\" or \"Revised\" License",
        "url": "https://api.github.com/licenses/bsd-3-clause"
      },
      "latest_release": {
        "tag_name": "v1.1.0",
        "published_at": "2026-10-26T15:05:15Z",
        "html_url": "https://github.com/tudat-team/tudatpy/releases/tag/v1.1.0"
      },
      "observed_at": "2026-11-09T16:04:50Z",
      "source_status": "ok",
      "stars_delta_7d": 3,
      "stars_baseline_at": "2026-11-02T09:40:30Z",
      "attempted_at": "2026-11-09T16:04:49Z",
      "refresh_status": "refreshed"
    },
    {
      "id": "pagmo2",
      "name": "pagmo",
      "repo": "esa/pagmo2",
      "fields": [
        "우주",
        "제조",
        "에너지"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [
        "C++",
        "병렬 최적화",
        "다목적 최적화",
        "전역 탐색"
      ],
      "summary_ko": "병렬 전역·다목적 최적화 알고리즘을 제공하는 C++ 라이브러리.",
      "design_note": "궤도·설계·에너지 모델의 목적함수와 연결해 대안 공간을 탐색하는 용도로 검토한다.",
      "operations_note": "모델·제약의 구현과 계산 예산·난수 조건을 기록하며 전역 최적해를 보장한다고 해석하지 않는다.",
      "full_name": "esa/pagmo2",
      "html_url": "https://github.com/esa/pagmo2",
      "description": "A C++ platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.",
      "stargazers_count": 939,
      "forks_count": 192,
      "open_issues_count": 38,
      "pushed_at": "2026-09-29T12:39:17Z",
      "updated_at": "2026-11-07T03:24:48Z",
      "archived": false,
      "created_at": "2017-01-08T21:53:43Z",
      "language": "C++",
      "fork": false,
      "github_id": 78372913,
      "license": {
        "spdx_id": "GPL-3.0",
        "name": "GNU General Public License v3.0",
        "url": "https://api.github.com/licenses/gpl-3.0"
      },
      "latest_release": {
        "tag_name": "v2.20.0",
        "published_at": "2026-09-25T12:40:07Z",
        "html_url": "https://github.com/esa/pagmo2/releases/tag/v2.20.0"
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        ":",
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      "abstract": "We present Isaac Lab, the natural successor to Isaac Gym, which extends the paradigm of GPU-native robotics simulation into the era of large-scale multi-modal learning. Isaac Lab combines high-fidelity GPU parallel physics, photorealistic rendering, and a modular, composable architecture for designing environments and training robot policies. Beyond physics and rendering, the framework integrates actuator models, multi-frequency sensor simulation, data collection pipelines, and domain randomization tools, unifying best practices for reinforcement and imitation learning at scale within a single extensible platform. We highlight its application to a diverse set of challenges, including whole-body control, cross-embodiment mobility, contact-rich and dexterous manipulation, and the integration of human demonstrations for skill acquisition. Finally, we discuss upcoming integration with the differentiable, GPU-accelerated Newton physics engine, which promises new opportunities for scalable, data-efficient, and gradient-based approaches to robot learning. We believe Isaac Lab's combination of advanced simulation capabilities, rich sensing, and data-center scale execution will help unlock the next generation of breakthroughs in robotics research.",
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      "tags": [
        "전력계통",
        "발전·저장",
        "설비투자",
        "운용 최적화",
        "기초 논문"
      ],
      "summary_ko": "발전·저장·송전망과 다른 에너지 부문을 연결해 전력 시스템의 운용과 설비투자를 여러 기간에 걸쳐 분석하는 공개 도구를 소개합니다.",
      "design_note": "발전·저장·송전 설비의 대안을 동일한 수요·비용·네트워크 가정으로 비교하는 모델 구성을 살펴볼 수 있습니다.",
      "operations_note": "시간별 수요·재생에너지 가용량과 운용 제약을 확인해야 합니다. 선형화한 전력 흐름의 결과를 상세 동특성이나 실제 계통 운용 검증과 구분합니다.",
      "authors": [
        "Tom Brown",
        "Jonas Hörsch",
        "David Schlachtberger"
      ],
      "published_at": "2017-07-31T15:12:36Z",
      "updated_at": "2018-01-17T15:54:11Z",
      "abstract": "Python for Power System Analysis (PyPSA) is a free software toolbox for simulating and optimising modern electrical power systems over multiple periods. PyPSA includes models for conventional generators with unit commitment, variable renewable generation, storage units, coupling to other energy sectors, and mixed alternating and direct current networks. It is designed to be easily extensible and to scale well with large networks and long time series. In this paper the basic functionality of PyPSA is described, including the formulation of the full power flow equations and the multi-period optimisation of operation and investment with linear power flow equations. PyPSA is positioned in the existing free software landscape as a bridge between traditional power flow analysis tools for steady-state analysis and full multi-period energy system models. The functionality is demonstrated on two open datasets of the transmission system in Germany (based on SciGRID) and Europe (based on GridKit).",
      "source_url": "https://arxiv.org/abs/1707.09913",
      "doi": "10.5334/jors.188",
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "id": "digital-twin-enabling-technologies",
      "arxiv_id": "1911.01276",
      "title": "Digital Twin: Enabling Technologies, Challenges and Open Research",
      "fields": [
        "제조"
      ],
      "techniques": [
        "디지털트윈",
        "AI",
        "데이터 분석"
      ],
      "tags": [
        "문헌 조사",
        "물리·가상 연결",
        "IoT",
        "도입 과제",
        "기초 논문"
      ],
      "summary_ko": "제조·의료·스마트시티의 디지털트윈 연구를 분류하고 AI·IoT 등 기반 기술과 해결해야 할 과제를 검토한 문헌 조사입니다.",
      "design_note": "단순한 가상 모델과 물리 대상의 데이터 연결을 구분하고, 필요한 데이터 흐름과 기술 구성요소를 정의할 때 참고할 수 있습니다.",
      "operations_note": "실제 운영 데이터 연결·갱신·품질 관리의 범위를 먼저 정해야 합니다. 이 문헌 조사를 특정 제품의 성능이나 현장 적용 성공의 증거로 사용하지 않습니다.",
      "authors": [
        "Aidan Fuller",
        "Zhong Fan",
        "Charles Day",
        "Chris Barlow"
      ],
      "published_at": "2019-10-29T15:16:21Z",
      "updated_at": "2020-06-18T15:58:27Z",
      "abstract": "Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these areas and the current state of research. The paper provides an assessment of the enabling technologies, challenges and open research for Digital Twins.",
      "source_url": "https://arxiv.org/abs/1911.01276",
      "doi": "10.1109/ACCESS.2020.2998358",
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "id": "brax",
      "arxiv_id": "2106.13281",
      "title": "Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation",
      "fields": [
        "로봇"
      ],
      "techniques": [
        "AI",
        "시뮬레이션",
        "최적화"
      ],
      "tags": [
        "미분가능 시뮬레이션",
        "강체 동역학",
        "JAX",
        "강화학습",
        "기초 논문"
      ],
      "summary_ko": "JAX와 가속기 병렬화를 이용해 강체 시뮬레이션과 정책 학습을 같은 장치에서 실행하는 물리 엔진을 소개합니다.",
      "design_note": "많은 환경을 병렬로 시험하는 로봇 제어 연구에서 물리 모델과 학습 연산의 배치를 설계할 때 살펴볼 수 있습니다.",
      "operations_note": "접촉·시간 간격·수치 계산 조건에 따른 결과를 확인해야 합니다. 논문의 계산 성능을 다른 하드웨어나 현재 버전의 보장값으로 사용하지 않습니다.",
      "authors": [
        "C. Daniel Freeman",
        "Erik Frey",
        "Anton Raichuk",
        "Sertan Girgin",
        "Igor Mordatch",
        "Olivier Bachem"
      ],
      "published_at": "2021-06-24T19:09:12Z",
      "updated_at": "2021-06-24T19:09:12Z",
      "abstract": "We present Brax, an open source library for rigid body simulation with a focus on performance and parallelism on accelerators, written in JAX. We present results on a suite of tasks inspired by the existing reinforcement learning literature, but remade in our engine. Additionally, we provide reimplementations of PPO, SAC, ES, and direct policy optimization in JAX that compile alongside our environments, allowing the learning algorithm and the environment processing to occur on the same device, and to scale seamlessly on accelerators. Finally, we include notebooks that facilitate training of performant policies on common OpenAI Gym MuJoCo-like tasks in minutes.",
      "source_url": "https://arxiv.org/abs/2106.13281",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "id": "difftaichi",
      "arxiv_id": "1910.00935",
      "title": "DiffTaichi: Differentiable Programming for Physical Simulation",
      "fields": [
        "제조",
        "로봇"
      ],
      "techniques": [
        "시뮬레이션",
        "최적화",
        "AI"
      ],
      "tags": [
        "미분가능 프로그래밍",
        "물리 모델",
        "기울기 기반 최적화",
        "기초 논문"
      ],
      "summary_ko": "물리 시뮬레이션의 기울기를 계산하는 프로그래밍 방법을 제시하고 여러 물리 모델의 학습·최적화 예제로 설명합니다.",
      "design_note": "설계 변수나 제어 입력을 바꾸어 목표 거동에 접근하는 기울기 기반 실험을 구성할 때 검토할 방법론입니다.",
      "operations_note": "관측 데이터와 모델 파라미터가 목적에 맞는지 확인해야 합니다. 계산된 기울기와 최적화 결과의 유효 범위는 대상 물리·수치 모델별로 확인합니다.",
      "authors": [
        "Yuanming Hu",
        "Luke Anderson",
        "Tzu-Mao Li",
        "Qi Sun",
        "Nathan Carr",
        "Jonathan Ragan-Kelley",
        "Frédo Durand"
      ],
      "published_at": "2019-10-01T05:00:26Z",
      "updated_at": "2020-02-14T06:21:07Z",
      "abstract": "We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators. Based on an imperative programming language, DiffTaichi generates gradients of simulation steps using source code transformations that preserve arithmetic intensity and parallelism. A light-weight tape is used to record the whole simulation program structure and replay the gradient kernels in a reversed order, for end-to-end backpropagation. We demonstrate the performance and productivity of our language in gradient-based learning and optimization tasks on 10 different physical simulators. For example, a differentiable elastic object simulator written in our language is 4.2x shorter than the hand-engineered CUDA version yet runs as fast, and is 188x faster than the TensorFlow implementation. Using our differentiable programs, neural network controllers are typically optimized within only tens of iterations.",
      "source_url": "https://arxiv.org/abs/1910.00935",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "id": "flatland-rl",
      "arxiv_id": "2012.05893",
      "title": "Flatland-RL : Multi-Agent Reinforcement Learning on Trains",
      "fields": [
        "철도"
      ],
      "techniques": [
        "AI",
        "시뮬레이션",
        "최적화"
      ],
      "tags": [
        "열차 재스케줄링",
        "다중 에이전트",
        "강화학습",
        "격자 환경",
        "기초 논문"
      ],
      "summary_ko": "열차 운행 재스케줄링 연구를 빠르게 반복할 수 있도록 단순화한 2차원 철도 환경과 강화·모방학습 실험을 소개합니다.",
      "design_note": "열차 경로와 자원 충돌을 단순한 환경에 모델링하고 운행 의사결정 알고리즘을 비교하는 연구 설계에 적합한 출발점입니다.",
      "operations_note": "실제 철도 운영에 필요한 신호·제동·안전·시간표 제약이 얼마나 표현되는지 별도 확인해야 합니다. 격자 환경의 결과를 현장 운행 가능 판정으로 사용하지 않습니다.",
      "authors": [
        "Sharada Mohanty",
        "Erik Nygren",
        "Florian Laurent",
        "Manuel Schneider",
        "Christian Scheller",
        "Nilabha Bhattacharya",
        "Jeremy Watson",
        "Adrian Egli",
        "Christian Eichenberger",
        "Christian Baumberger",
        "Gereon Vienken",
        "Irene Sturm",
        "Guillaume Sartoretti",
        "Giacomo Spigler"
      ],
      "published_at": "2020-12-10T18:54:27Z",
      "updated_at": "2020-12-11T14:51:22Z",
      "abstract": "Efficient automated scheduling of trains remains a major challenge for modern railway systems. The underlying vehicle rescheduling problem (VRSP) has been a major focus of Operations Research (OR) since decades. Traditional approaches use complex simulators to study VRSP, where experimenting with a broad range of novel ideas is time consuming and has a huge computational overhead. In this paper, we introduce a two-dimensional simplified grid environment called \"Flatland\" that allows for faster experimentation. Flatland does not only reduce the complexity of the full physical simulation, but also provides an easy-to-use interface to test novel approaches for the VRSP, such as Reinforcement Learning (RL) and Imitation Learning (IL). In order to probe the potential of Machine Learning (ML) research on Flatland, we (1) ran a first series of RL and IL experiments and (2) design and executed a public Benchmark at NeurIPS 2020 to engage a large community of researchers to work on this problem. Our own experimental results, on the one hand, demonstrate that ML has potential in solving the VRSP on Flatland. On the other hand, we identify key topics that need further research. Overall, the Flatland environment has proven to be a robust and valuable framework to investigate the VRSP for railway networks. Our experiments provide a good starting point for further research and for the participants of the NeurIPS 2020 Flatland Benchmark. All of these efforts together have the potential to have a substantial impact on shaping the mobility of the future.",
      "source_url": "https://arxiv.org/abs/2012.05893",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "id": "flow-mixed-autonomy",
      "arxiv_id": "1710.05465",
      "title": "Flow: A Modular Learning Framework for Mixed Autonomy Traffic",
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "AI",
        "시뮬레이션",
        "최적화"
      ],
      "tags": [
        "혼합 교통",
        "자율주행",
        "강화학습",
        "교통 제어",
        "기초 논문"
      ],
      "summary_ko": "사람이 운전하는 차량과 자율주행 차량이 섞인 교통에서 제어 정책과 교통 흐름의 영향을 연구하는 모듈형 학습 프레임워크입니다.",
      "design_note": "자율주행 차량 비율·관측 범위·도로 구성을 바꿔 네트워크 수준의 영향을 비교하는 실험 설계를 참고할 수 있습니다.",
      "operations_note": "차량 행동·교통 밀도·관측·제어 조건이 바뀔 때 정책이 유지되는지 확인해야 합니다. 시뮬레이션 성과를 실제 도로의 안전성·혼잡 개선 보장으로 해석하지 않습니다.",
      "authors": [
        "Cathy Wu",
        "Aboudy Kreidieh",
        "Kanaad Parvate",
        "Eugene Vinitsky",
        "Alexandre M Bayen"
      ],
      "published_at": "2017-10-16T01:51:51Z",
      "updated_at": "2021-12-30T22:53:50Z",
      "abstract": "The rapid development of autonomous vehicles (AVs) holds vast potential for transportation systems through improved safety, efficiency, and access to mobility. However, the progression of these impacts, as AVs are adopted, is not well understood. Numerous technical challenges arise from the goal of analyzing the partial adoption of autonomy: partial control and observation, multi-vehicle interactions, and the sheer variety of scenarios represented by real-world networks. To shed light into near-term AV impacts, this article studies the suitability of deep reinforcement learning (RL) for overcoming these challenges in a low AV-adoption regime. A modular learning framework is presented, which leverages deep RL to address complex traffic dynamics. Modules are composed to capture common traffic phenomena (stop-and-go traffic jams, lane changing, intersections). Learned control laws are found to improve upon human driving performance, in terms of system-level velocity, by up to 57% with only 4-7% adoption of AVs. Furthermore, in single-lane traffic, a small neural network control law with only local observation is found to eliminate stop-and-go traffic - surpassing all known model-based controllers to achieve near-optimal performance - and generalize to out-of-distribution traffic densities.",
      "source_url": "https://arxiv.org/abs/1710.05465",
      "doi": "10.1109/TRO.2021.3087314",
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "id": "cityflow",
      "arxiv_id": "1905.05217",
      "title": "CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario",
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "AI",
        "시뮬레이션"
      ],
      "tags": [
        "도시 교통",
        "신호 제어",
        "다중 에이전트",
        "교통 수요",
        "기초 논문"
      ],
      "summary_ko": "큰 도로망과 교통 수요를 바탕으로 다중 에이전트 강화학습을 실험할 수 있는 교통 시뮬레이션 환경을 소개합니다.",
      "design_note": "다수 교차로의 신호 제어를 비교할 때 도로망·교통 수요·에이전트의 경계를 구성하는 방법을 참고할 수 있습니다.",
      "operations_note": "수요 추정과 차량 경로·신호 제약을 실제 대상에 맞춰 확인해야 합니다. 논문의 속도 비교 결과를 현재 도구 버전이나 다른 시나리오에 그대로 적용하지 않습니다.",
      "authors": [
        "Huichu Zhang",
        "Siyuan Feng",
        "Chang Liu",
        "Yaoyao Ding",
        "Yichen Zhu",
        "Zihan Zhou",
        "Weinan Zhang",
        "Yong Yu",
        "Haiming Jin",
        "Zhenhui Li"
      ],
      "published_at": "2019-05-13T18:07:41Z",
      "updated_at": "2019-05-13T18:07:41Z",
      "abstract": "Traffic signal control is an emerging application scenario for reinforcement learning. Besides being as an important problem that affects people's daily life in commuting, traffic signal control poses its unique challenges for reinforcement learning in terms of adapting to dynamic traffic environment and coordinating thousands of agents including vehicles and pedestrians. A key factor in the success of modern reinforcement learning relies on a good simulator to generate a large number of data samples for learning. The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios. This motivates us to create a new traffic simulator CityFlow with fundamentally optimized data structures and efficient algorithms. CityFlow can support flexible definitions for road network and traffic flow based on synthetic and real-world data. It also provides user-friendly interface for reinforcement learning. Most importantly, CityFlow is more than twenty times faster than SUMO and is capable of supporting city-wide traffic simulation with an interactive render for monitoring. Besides traffic signal control, CityFlow could serve as the base for other transportation studies and can create new possibilities to test machine learning methods in the intelligent transportation domain.",
      "source_url": "https://arxiv.org/abs/1905.05217",
      "doi": "10.1145/3308558.3314139",
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "id": "jump-modeling-language",
      "arxiv_id": "1508.01982",
      "title": "JuMP: A Modeling Language for Mathematical Optimization",
      "fields": [
        "제조",
        "물류",
        "에너지"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [
        "수리 모델링",
        "선형계획",
        "혼합정수계획",
        "Julia",
        "기초 논문"
      ],
      "summary_ko": "선형·혼합정수·비선형 등 여러 최적화 문제를 고수준 대수식으로 표현하는 Julia 기반 모델링 언어의 구조와 확장 방법을 설명합니다.",
      "design_note": "설비·자원·배치 등의 의사결정을 목적함수와 제약으로 정식화하는 도구 후보입니다. 산업별 적용 문제와 모델의 적합성은 별도 편집 판단입니다.",
      "operations_note": "문제의 해 존재 여부·계산 시간·최적성 간격과 실제 입력 데이터를 확인해야 합니다. 모델링 언어와 이를 실행하는 최적화 솔버의 기능·조건을 구분합니다.",
      "authors": [
        "Iain Dunning",
        "Joey Huchette",
        "Miles Lubin"
      ],
      "published_at": "2015-08-09T03:55:19Z",
      "updated_at": "2016-08-15T04:27:52Z",
      "abstract": "JuMP is an open-source modeling language that allows users to express a wide range of optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and nonlinear) in a high-level, algebraic syntax. JuMP takes advantage of advanced features of the Julia programming language to offer unique functionality while achieving performance on par with commercial modeling tools for standard tasks. In this work we will provide benchmarks, present the novel aspects of the implementation, and discuss how JuMP can be extended to new problem classes and composed with state-of-the-art tools for visualization and interactivity.",
      "source_url": "https://arxiv.org/abs/1508.01982",
      "doi": "10.1137/15M1020575",
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "id": "graph-network-physics",
      "arxiv_id": "2002.09405",
      "title": "Learning to Simulate Complex Physics with Graph Networks",
      "fields": [
        "제조",
        "토목공사"
      ],
      "techniques": [
        "AI",
        "시뮬레이션",
        "데이터 분석"
      ],
      "tags": [
        "그래프 신경망",
        "입자 기반 모델",
        "학습 기반 시뮬레이션",
        "일반화",
        "기초 논문"
      ],
      "summary_ko": "입자 간 상호작용을 그래프로 표현해 여러 재료의 물리적 변화를 학습하고 시뮬레이션하는 접근을 소개합니다.",
      "design_note": "재료·입자 거동의 빠른 대체 모델을 구상할 때 참고할 연구입니다. 제조·토목 태그는 검토할 적용 문제를 위한 분류이며 현장 검증을 뜻하지 않습니다.",
      "operations_note": "학습 범위를 벗어난 재료·경계조건과 긴 시간 예측에서 오차가 어떻게 변하는지 확인해야 합니다. 물리 솔버의 검증이나 공학적 안전 판정을 대체하지 않습니다.",
      "authors": [
        "Alvaro Sanchez-Gonzalez",
        "Jonathan Godwin",
        "Tobias Pfaff",
        "Rex Ying",
        "Jure Leskovec",
        "Peter W. Battaglia"
      ],
      "published_at": "2020-02-21T16:44:28Z",
      "updated_at": "2020-09-14T16:52:10Z",
      "abstract": "Here we present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and deformable materials interacting with one another. Our framework---which we term \"Graph Network-based Simulators\" (GNS)---represents the state of a physical system with particles, expressed as nodes in a graph, and computes dynamics via learned message-passing. Our results show that our model can generalize from single-timestep predictions with thousands of particles during training, to different initial conditions, thousands of timesteps, and at least an order of magnitude more particles at test time. Our model was robust to hyperparameter choices across various evaluation metrics: the main determinants of long-term performance were the number of message-passing steps, and mitigating the accumulation of error by corrupting the training data with noise. Our GNS framework advances the state-of-the-art in learned physical simulation, and holds promise for solving a wide range of complex forward and inverse problems.",
      "source_url": "https://arxiv.org/abs/2002.09405",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-19T00:44:45Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robots",
            "Robotic"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2610-22372",
      "arxiv_id": "2610.22372",
      "title": "LIBERO-PeRM: Benchmarking Personalized Robotic Manipulation",
      "summary_ko": "Personalization is essential for general-purpose robots to transition into household environments and achieve true practicality. While general robotic manipulation has been extensively studied, personalized robotic manipulation (PeRM) lacks a controlled benchmark for separating task completion from preference satisfaction. We introduce LIBERO-PeRM, a benchmark for preference learning in embodied agents. LIBERO-PeRM studies five questions: 1) how policies follow preferences in zero-shot settings; 2) how they learn single preferences from demonstrations; 3) how efficiently they acquire preferences as demonstrations increase; 4) how learned preferences generalize across layouts and tasks; and 5) how policies compose preferences and behave under conflicts. We propose a controlled formulation and taxonomy of observable preferences in simulation, covering motion, skill, and user-profile choices. This formulation is restricted to language-expressible preferences that can be instantiated in LIBERO without changing the task goal, evaluated automatically, and reused across tasks. Building on LIBERO, our procedural pipeline generates paired demonstrations with and without preferences. We create five task suites (312 tasks) with high-quality demonstrations, rich metadata, and preference-satisfaction metrics. Experiments show that current VLA policies struggle with zero-shot personalization, while SFT makes preferences learnable. Mixed training achieves better no-preference retention than sequential adaptation and improves performance with contextual prompts compared with preference-only training, but can weaken motion-preference learning; preference generalization, decomposition, and conflict handling remain difficult. The code, datasets, and demos can be found on the project website: https://libero-perm.github.io/.",
      "authors": [
        "Zhixu Li",
        "Keqian Tang",
        "Litian Gong",
        "Jingyu Yao",
        "Wenqian Zhang",
        "Tianze Xu",
        "Zehao Wang",
        "Yuping Wang",
        "Junge Zhang",
        "Jiachen Li"
      ],
      "published_at": "2026-10-15T17:59:59Z",
      "updated_at": "2026-10-15T17:59:59Z",
      "abstract": "Personalization is essential for general-purpose robots to transition into household environments and achieve true practicality. While general robotic manipulation has been extensively studied, personalized robotic manipulation (PeRM) lacks a controlled benchmark for separating task completion from preference satisfaction. We introduce LIBERO-PeRM, a benchmark for preference learning in embodied agents. LIBERO-PeRM studies five questions: 1) how policies follow preferences in zero-shot settings; 2) how they learn single preferences from demonstrations; 3) how efficiently they acquire preferences as demonstrations increase; 4) how learned preferences generalize across layouts and tasks; and 5) how policies compose preferences and behave under conflicts. We propose a controlled formulation and taxonomy of observable preferences in simulation, covering motion, skill, and user-profile choices. This formulation is restricted to language-expressible preferences that can be instantiated in LIBERO without changing the task goal, evaluated automatically, and reused across tasks. Building on LIBERO, our procedural pipeline generates paired demonstrations with and without preferences. We create five task suites (312 tasks) with high-quality demonstrations, rich metadata, and preference-satisfaction metrics. Experiments show that current VLA policies struggle with zero-shot personalization, while SFT makes preferences learnable. Mixed training achieves better no-preference retention than sequential adaptation and improves performance with contextual prompts compared with preference-only training, but can weaken motion-preference learning; preference generalization, decomposition, and conflict handling remain difficult. The code, datasets, and demos can be found on the project website: https://libero-perm.github.io/.",
      "source_url": "https://arxiv.org/abs/2610.22372",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "로봇",
        "제조"
      ],
      "techniques": [
        "AI",
        "시뮬레이션",
        "디지털트윈"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-19T00:44:45Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "Robotic"
          ],
          "제조": [
            "manufacturing"
          ]
        },
        "technique_matches": {
          "AI": [
            "neural-network",
            "neural network"
          ],
          "시뮬레이션": [
            "simulation"
          ],
          "디지털트윈": [
            "digital twin",
            "Digital Twins"
          ]
        }
      },
      "id": "auto-arxiv-2610-21939",
      "arxiv_id": "2610.21939",
      "title": "De-Risking Robotic Welding for In-Space Metal Joining Through Physics-Grounded Digital Twins",
      "summary_ko": "Growing interest in in-space servicing, manufacturing, and assembly has renewed attention on reliable metal joining for modular construction and on-orbit repair. Robotic welding is promising, but reduced-gravity and low-pressure operation remains poorly characterised and flight-like tests are limited. The UK Space Agency-supported ISPARK project is developing a physics-grounded digital twin for structured process de-risking. This paper evaluates a model-based prototype combining a finite-volume thermal weld-pool model, Gaussian-process (GP) and neural-network surrogates, and a simulated six-axis manipulator. The reduced model includes solid-liquid phase change, effective thermocapillary transport, and a local-pressure parameter. Gravity is assessed separately through a hydrostatic-to-capillary pressure ratio and does not enter the thermal energy equation. Across repeated five-fold cross-validation, the GP has lower normalised mean absolute error than the neural network for all eight outputs. In 10 held-out boundary cases, 74 of 80 solver responses fall within nominal 90% GP predictive intervals. A three-output GP query takes 249 microseconds, including scaling and predictive variance. In a simulated UR5 L-shaped-seam study, a proportional controller adjusts the synergic power-source setting as travel speed falls near the corner. Finite-volume re-evaluation finds all 12 closed-loop operating points and 9 of 12 open-loop points within an illustrative 1.0-3.0 mm root-side-width band. These simulation results support using the framework to screen welding conditions and control strategies before experimental tests. Thermal-vacuum, targeted reduced-gravity, and robotic hardware-in-the-loop studies are needed to assess the model and controller.",
      "authors": [
        "Zhou Daniel Hao",
        "Umer Masood Chaudry",
        "Robert Shaw",
        "Andrew Cheney",
        "Nick Ludford",
        "Mark Sims",
        "Hongbiao Dong"
      ],
      "published_at": "2026-10-15T14:14:10Z",
      "updated_at": "2026-10-15T14:14:10Z",
      "abstract": "Growing interest in in-space servicing, manufacturing, and assembly has renewed attention on reliable metal joining for modular construction and on-orbit repair. Robotic welding is promising, but reduced-gravity and low-pressure operation remains poorly characterised and flight-like tests are limited. The UK Space Agency-supported ISPARK project is developing a physics-grounded digital twin for structured process de-risking. This paper evaluates a model-based prototype combining a finite-volume thermal weld-pool model, Gaussian-process (GP) and neural-network surrogates, and a simulated six-axis manipulator. The reduced model includes solid-liquid phase change, effective thermocapillary transport, and a local-pressure parameter. Gravity is assessed separately through a hydrostatic-to-capillary pressure ratio and does not enter the thermal energy equation. Across repeated five-fold cross-validation, the GP has lower normalised mean absolute error than the neural network for all eight outputs. In 10 held-out boundary cases, 74 of 80 solver responses fall within nominal 90% GP predictive intervals. A three-output GP query takes 249 microseconds, including scaling and predictive variance. In a simulated UR5 L-shaped-seam study, a proportional controller adjusts the synergic power-source setting as travel speed falls near the corner. Finite-volume re-evaluation finds all 12 closed-loop operating points and 9 of 12 open-loop points within an illustrative 1.0-3.0 mm root-side-width band. These simulation results support using the framework to screen welding conditions and control strategies before experimental tests. Thermal-vacuum, targeted reduced-gravity, and robotic hardware-in-the-loop studies are needed to assess the model and controller.",
      "source_url": "https://arxiv.org/abs/2610.21939",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-19T00:44:45Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "energy systems"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation",
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2610-21694",
      "arxiv_id": "2610.21694",
      "title": "Hurricane Laura-induced loads on wind turbines -- Part I: High-fidelity modeling and sensitivity to turbulent inflow conditions",
      "summary_ko": "Tropical cyclones can produce extreme winds in the lowest portion of the boundary layer, posing high risks to critical energy infrastructure, including wind energy systems. This study evaluates the dynamic response of an idling wind turbine to turbulent inflow generated during the passage of a hurricane. We performed large-eddy simulations of Hurricane Laura (2020) at several grid resolutions -- coarse $(Δx=55.55\\,{\\rm m})$, intermediate $(Δx=25\\,{\\rm m})$, and fine $(Δx=10\\,{\\rm m})$ -- to investigate differences in mean flow characteristics and turbulence structures as the hurricane progresses across a virtual wind turbine location. The large-eddy simulation data served as direct inflow conditions for aeroelastic simulations to assess the turbine's response. Our results reveal significant differences in the mean wind and turbulent flow structures between the grid resolutions, leading to distinct impacts on the wind turbine's aeroelastic behavior. These findings emphasize the need for high-resolution inflow information to better predict extreme loads and inform the design of wind turbines in regions frequently affected by tropical cyclones. As a continuation of this work, Part II of this study will evaluate the turbine's response from different locations relative to the storm's trajectory and compare it with current wind turbine design practices.",
      "authors": [
        "Miguel Sanchez-Gomez",
        "Lucas Carmo",
        "Matthew Churchfield",
        "Jason Jonkman",
        "Walter Musial"
      ],
      "published_at": "2026-10-15T11:16:46Z",
      "updated_at": "2026-10-15T11:16:46Z",
      "abstract": "Tropical cyclones can produce extreme winds in the lowest portion of the boundary layer, posing high risks to critical energy infrastructure, including wind energy systems. This study evaluates the dynamic response of an idling wind turbine to turbulent inflow generated during the passage of a hurricane. We performed large-eddy simulations of Hurricane Laura (2020) at several grid resolutions -- coarse $(Δx=55.55\\,{\\rm m})$, intermediate $(Δx=25\\,{\\rm m})$, and fine $(Δx=10\\,{\\rm m})$ -- to investigate differences in mean flow characteristics and turbulence structures as the hurricane progresses across a virtual wind turbine location. The large-eddy simulation data served as direct inflow conditions for aeroelastic simulations to assess the turbine's response. Our results reveal significant differences in the mean wind and turbulent flow structures between the grid resolutions, leading to distinct impacts on the wind turbine's aeroelastic behavior. These findings emphasize the need for high-resolution inflow information to better predict extreme loads and inform the design of wind turbines in regions frequently affected by tropical cyclones. As a continuation of this work, Part II of this study will evaluate the turbine's response from different locations relative to the storm's trajectory and compare it with current wind turbine design practices.",
      "source_url": "https://arxiv.org/abs/2610.21694",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "AI",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-19T00:44:45Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "도로교통": [
            "Traffic Signal"
          ]
        },
        "technique_matches": {
          "AI": [
            "machine-learning"
          ],
          "최적화": [
            "Optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-21883",
      "arxiv_id": "2610.21883",
      "title": "Hybrid Quantum-Classical Optimization for Emergency-Priority Traffic Signal Control",
      "summary_ko": "Emergency-priority traffic signal control requires reducing emergency-vehicle delay while limiting disruption to ordinary traffic. This work investigates a finite-horizon QUBO formulation and compares Exact QUBO optimization, QAOA, CVaR-QAOA, a quantum machine-learning policy, fixed-time control, and max-pressure control in a three-intersection corridor. The study evaluates three traffic-demand regimes, examines the effect of the emergency-priority weight, and compares quantum solutions with the exact QUBO optimum. The optimization-based controllers achieved reliable emergency progression across all tested demand regimes, while max-pressure produced lower ordinary-traffic delay. Increasing emergency priority reduced emergency travel time but increased congestion, with diminishing improvements at larger weights. CVaR-QAOA reproduced the exact QUBO solution more accurately than standard QAOA, although the resulting differences in closed-loop traffic performance were comparatively small.",
      "authors": [
        "Silvie Illésová",
        "Vojtěch Novák"
      ],
      "published_at": "2026-10-15T13:47:26Z",
      "updated_at": "2026-10-15T13:47:26Z",
      "abstract": "Emergency-priority traffic signal control requires reducing emergency-vehicle delay while limiting disruption to ordinary traffic. This work investigates a finite-horizon QUBO formulation and compares Exact QUBO optimization, QAOA, CVaR-QAOA, a quantum machine-learning policy, fixed-time control, and max-pressure control in a three-intersection corridor. The study evaluates three traffic-demand regimes, examines the effect of the emergency-priority weight, and compares quantum solutions with the exact QUBO optimum. The optimization-based controllers achieved reliable emergency progression across all tested demand regimes, while max-pressure produced lower ordinary-traffic delay. Increasing emergency priority reduced emergency travel time but increased congestion, with diminishing improvements at larger weights. CVaR-QAOA reproduced the exact QUBO solution more accurately than standard QAOA, although the resulting differences in closed-loop traffic performance were comparatively small.",
      "source_url": "https://arxiv.org/abs/2610.21883",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "건설"
      ],
      "techniques": [
        "최적화",
        "데이터 분석"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-19T00:44:45Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "건설": [
            "construction-site"
          ]
        },
        "technique_matches": {
          "최적화": [
            "optimization"
          ],
          "데이터 분석": [
            "3D reconstruction"
          ]
        }
      },
      "id": "auto-arxiv-2609-13504",
      "arxiv_id": "2609.13504",
      "title": "RIGOR: Rig-Informed Geometry for Omnidirectional Reconstruction",
      "summary_ko": "Recent developments in feed-forward 3D reconstruction resulted in models which can recover dense scene representations and camera motion solely from an image stream. However, such predictions are prone to becoming inconsistent over long trajectories, specifically in demanding environments with repetitive structures, weak textures and dynamic objects or people. One way to mitigate those challenges is to use an omnidirectional camera, which provides wide spatial coverage and captures richer visual information. Yet, the majority of models do not offer support for 360-degree imagery or require additional fine-tuning. To bridge these two aspects, we present RIGOR: a large-scale reconstruction pipeline for gravity-aligned omnidirectional videos that retains a frozen feed-forward perspective backbone and exploits each panorama as a four-view virtual rig. The rig structure is used to detect and repair locally inconsistent predictions, to retrieve loop closures through cyclic four-view consensus, and to geometrically verify candidate revisits before global optimization. Verified constraints drive a Sim(3) pose graph that corrects accumulated rotation, translation, and scale drift along the sequence. We demonstrate that the proposed consistency mechanisms improve both trajectory accuracy and reconstructed geometry over a feed-forward baseline on challenging construction-site sequences. The code is made available under this link: https://github.com/TangentH/RIGOR.",
      "authors": [
        "Tingjun Huang",
        "Dmitry Rudshin",
        "Mathieu Meyer",
        "Pietro Bonazzi",
        "Marc Pollefeys",
        "Emilia Szymańska"
      ],
      "published_at": "2026-09-11T20:11:43Z",
      "updated_at": "2026-10-06T09:21:39Z",
      "abstract": "Recent developments in feed-forward 3D reconstruction resulted in models which can recover dense scene representations and camera motion solely from an image stream. However, such predictions are prone to becoming inconsistent over long trajectories, specifically in demanding environments with repetitive structures, weak textures and dynamic objects or people. One way to mitigate those challenges is to use an omnidirectional camera, which provides wide spatial coverage and captures richer visual information. Yet, the majority of models do not offer support for 360-degree imagery or require additional fine-tuning. To bridge these two aspects, we present RIGOR: a large-scale reconstruction pipeline for gravity-aligned omnidirectional videos that retains a frozen feed-forward perspective backbone and exploits each panorama as a four-view virtual rig. The rig structure is used to detect and repair locally inconsistent predictions, to retrieve loop closures through cyclic four-view consensus, and to geometrically verify candidate revisits before global optimization. Verified constraints drive a Sim(3) pose graph that corrects accumulated rotation, translation, and scale drift along the sequence. We demonstrate that the proposed consistency mechanisms improve both trajectory accuracy and reconstructed geometry over a feed-forward baseline on challenging construction-site sequences. The code is made available under this link: https://github.com/TangentH/RIGOR.",
      "source_url": "https://arxiv.org/abs/2609.13504",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "철도"
      ],
      "techniques": [
        "AI"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-19T00:44:45Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "철도": [
            "railway"
          ]
        },
        "technique_matches": {
          "AI": [
            "neural network"
          ]
        }
      },
      "id": "auto-arxiv-2610-07083",
      "arxiv_id": "2610.07083",
      "title": "Graph-Based Recognition of Simulated Train-Driver States From Facial and Upper-Body Keypoints",
      "summary_ko": "Driver fatigue poses a significant challenge to railway safety, with traditional systems like the dead-man switch offering limited and basic alertness checks. This study presents a vision-based monitoring system that relies solely on a single front-facing RGB camera and a graph neural network to classify simulated train-driver states into alert, not-alert, and an emergency class comprising acted emergency-like behaviours. To optimize input representations for the model, an ablation study was performed, comparing three feature configurations: skeletal-only, facial-only, and a combination of both. Experimental results show that combining facial and skeletal features yields the highest accuracy (81%) for the three-class model under the light condition, outperforming models that use only facial or skeletal features. Furthermore, the combination of facial and skeletal features achieves 99% accuracy in the alert/not alert classification in light condition. Additionally, we introduced a controlled RGB video dataset containing alert, not alert, and acted emergency-like behaviours recorded under three illumination conditions. These contributions represent a step toward passive and non-contact train-driver state recognition based on facial and upper-body dynamics.",
      "authors": [
        "Olivia Nocentini",
        "Marta Lagomarsino",
        "Gokhan Solak",
        "Younggeol Cho",
        "Qiyi Tong",
        "Sara Zeynalpour",
        "Marta Lorenzini",
        "Alessandro Ledda",
        "Arash Ajoudani"
      ],
      "published_at": "2026-10-05T11:25:16Z",
      "updated_at": "2026-10-28T19:05:05Z",
      "abstract": "Driver fatigue poses a significant challenge to railway safety, with traditional systems like the dead-man switch offering limited and basic alertness checks. This study presents a vision-based monitoring system that relies solely on a single front-facing RGB camera and a graph neural network to classify simulated train-driver states into alert, not-alert, and an emergency class comprising acted emergency-like behaviours. To optimize input representations for the model, an ablation study was performed, comparing three feature configurations: skeletal-only, facial-only, and a combination of both. Experimental results show that combining facial and skeletal features yields the highest accuracy (81%) for the three-class model under the light condition, outperforming models that use only facial or skeletal features. Furthermore, the combination of facial and skeletal features achieves 99% accuracy in the alert/not alert classification in light condition. Additionally, we introduced a controlled RGB video dataset containing alert, not alert, and acted emergency-like behaviours recorded under three illumination conditions. These contributions represent a step toward passive and non-contact train-driver state recognition based on facial and upper-body dynamics.",
      "source_url": "https://arxiv.org/abs/2610.07083",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "AI",
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-20T07:44:48Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robot",
            "robots"
          ]
        },
        "technique_matches": {
          "AI": [
            "reinforcement learning"
          ],
          "시뮬레이션": [
            "simulation",
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2610-23849",
      "arxiv_id": "2610.23849",
      "title": "Blind Precise Assembly: Proprioceptive Sim-to-Real Peg-in-Hole for Diverse Parts",
      "summary_ko": "Every robot already carries high-resolution contact sensors: its own joints. Legged robots and humanoids exploit them to walk, run, and track whole-body motions without vision, but there the contacts are large and forceful. Is joint sensing also enough for precise, contact-rich assembly, where the robot must find a millimeter-scale opening, mesh gear teeth, or catch a thread through light contact? We study this question by training blind assembly policies that observe only a short history of joint positions, velocities, and their own actions, without visual, force, torque, or tactile sensing. The poses of both the fixture and the part are unknown to the policy, and the grasp is randomized substantially. The policy must therefore assemble the parts through contact, sensed exclusively through its own proprioception. Trained with reinforcement learning entirely in simulation over randomized parts, grasps, and contacts, such policies learn peg insertion, gear meshing, and nut threading. The peg insertion policy transfers zero-shot to the real world and inserts pegs of different shapes and sizes in 73% of trials. Controlled comparisons with matched visual policies in simulation show that blind policies generalize far better to unseen part shapes and sizes, though they can be slower. Training such policies needs thousands of different assemblies, each simulated with its clearance intact, so we also release an open-source generator that builds them procedurally from exact convex pieces for MuJoCo; it is more accurate and faster than convex decomposition or MuJoCo's signed-distance collision. Videos and interactive simulations are at https://younghyopark.me/blind-proprioceptive-assembly/.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Antonia Bronars",
        "Younghyo Park",
        "Kushal Kedia",
        "Pulkit Agrawal"
      ],
      "published_at": "2026-10-16T17:58:48Z",
      "updated_at": "2026-10-16T17:58:48Z",
      "abstract": "Every robot already carries high-resolution contact sensors: its own joints. Legged robots and humanoids exploit them to walk, run, and track whole-body motions without vision, but there the contacts are large and forceful. Is joint sensing also enough for precise, contact-rich assembly, where the robot must find a millimeter-scale opening, mesh gear teeth, or catch a thread through light contact? We study this question by training blind assembly policies that observe only a short history of joint positions, velocities, and their own actions, without visual, force, torque, or tactile sensing. The poses of both the fixture and the part are unknown to the policy, and the grasp is randomized substantially. The policy must therefore assemble the parts through contact, sensed exclusively through its own proprioception. Trained with reinforcement learning entirely in simulation over randomized parts, grasps, and contacts, such policies learn peg insertion, gear meshing, and nut threading. The peg insertion policy transfers zero-shot to the real world and inserts pegs of different shapes and sizes in 73% of trials. Controlled comparisons with matched visual policies in simulation show that blind policies generalize far better to unseen part shapes and sizes, though they can be slower. Training such policies needs thousands of different assemblies, each simulated with its clearance intact, so we also release an open-source generator that builds them procedurally from exact convex pieces for MuJoCo; it is more accurate and faster than convex decomposition or MuJoCo's signed-distance collision. Videos and interactive simulations are at https://younghyopark.me/blind-proprioceptive-assembly/.",
      "source_url": "https://arxiv.org/abs/2610.23849",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "우주"
      ],
      "techniques": [
        "AI",
        "시뮬레이션",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-20T07:44:48Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "우주": [
            "Satellite"
          ]
        },
        "technique_matches": {
          "AI": [
            "Reinforcement-Learning",
            "reinforcement learning"
          ],
          "시뮬레이션": [
            "Simulation",
            "simulations"
          ],
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-23682",
      "arxiv_id": "2610.23682",
      "title": "Packet Loss Analysis Under Doppler Effects and Reinforcement-Learning-Based Resource Allocation for Direct-to-Satellite LoRaWAN",
      "summary_ko": "Long range wide area network (LoRaWAN) has a long communication range and low power consumption, making it a potential technology for direct-to-satellite (DtS) Internet of Things (IoT) connectivity. However, packet collisions and Doppler impairments brought on by satellite motion can greatly increase the packet loss ratio (PLR) in large-scale DtS-IoT networks. In this paper, we study resource allocation in DtSIoT LoRaWAN by first developing an analytical PLR model that simultaneously takes Doppler impairments and packet collisions in the temporal and frequency domains into account. In order to maximize resource allocation, the generated PLR is then used as the reward function for two reinforcement learning (RL) schemes: deep Q-network (DQN) and proximal policy optimization (PPO). Simulation results validate the analytical PLR model against Monte Carlo simulations and demonstrate significant improvements over random resource allocation. At a target PLR of 3*10^-2, while random allocation supports only 100 end devices (EDs), PPO and DQN support approximately 650 and 690 EDs, respectively, representing nearly a seven-fold increase. Furthermore, for 800 EDs, PPO and DQN increase the total number of transmitted packets by 106.7% and 88.0%, respectively, compared with random allocation.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Alireza Maleki",
        "Ebrahim Bedeer",
        "Robert Barton"
      ],
      "published_at": "2026-10-16T16:11:11Z",
      "updated_at": "2026-10-16T16:11:11Z",
      "abstract": "Long range wide area network (LoRaWAN) has a long communication range and low power consumption, making it a potential technology for direct-to-satellite (DtS) Internet of Things (IoT) connectivity. However, packet collisions and Doppler impairments brought on by satellite motion can greatly increase the packet loss ratio (PLR) in large-scale DtS-IoT networks. In this paper, we study resource allocation in DtSIoT LoRaWAN by first developing an analytical PLR model that simultaneously takes Doppler impairments and packet collisions in the temporal and frequency domains into account. In order to maximize resource allocation, the generated PLR is then used as the reward function for two reinforcement learning (RL) schemes: deep Q-network (DQN) and proximal policy optimization (PPO). Simulation results validate the analytical PLR model against Monte Carlo simulations and demonstrate significant improvements over random resource allocation. At a target PLR of 3*10^-2, while random allocation supports only 100 end devices (EDs), PPO and DQN support approximately 650 and 690 EDs, respectively, representing nearly a seven-fold increase. Furthermore, for 800 EDs, PPO and DQN increase the total number of transmitted packets by 106.7% and 88.0%, respectively, compared with random allocation.",
      "source_url": "https://arxiv.org/abs/2610.23682",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇",
        "토목공사"
      ],
      "techniques": [
        "시뮬레이션",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-20T07:44:48Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robot",
            "robots"
          ],
          "토목공사": [
            "tunneling"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ],
          "최적화": [
            "optimization",
            "mixed-integer"
          ]
        }
      },
      "id": "auto-arxiv-2610-23524",
      "arxiv_id": "2610.23524",
      "title": "QIEN: Quantum-Inspired Energy-Neutral UAV Trajectory for WPT-Enabled Industrial HRLLC",
      "summary_ko": "This paper introduces Quantum-Inspired Energy-Neutral (QIEN) UAV Trajectory for WPT-Enabled Industrial HRLLC, a novel framework for mobility-integrated trajectory and resource optimization in solar-powered uncrewed aerial vehicle (UAV) relay systems supporting autonomous mining robots in post-disaster environments. We formulate a mixed-integer nonlinear program (MINLP) that jointly optimizes UAV trajectory, finite blocklength allocation, transmit power, and wireless power transfer (WPT) over discrete time horizons to maximize aggregate data throughput. The system operates under strict extreme hyper-reliable low-latency communication (HRLLC) constraints, energy sustainability requirements via solar harvesting, and continuous robot power delivery. To solve this large-scale optimization problem, QIEN employs a hybrid Cooperative Quantum-behaved Particle Swarm Optimization (Co-QPSO) algorithm using quantum tunneling mechanisms and a heterogeneous swarm architecture. Our simulation results demonstrate that the proposed QIEN framework significantly outperforms benchmark algorithms, achieving successful convergence while maintaining energy-neutral operation and satisfying all reliability, power, and mobility constraints.",
      "doi": "10.1109/TVT.2026.3742230",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Ali Ranjha",
        "Georges Kaddoum",
        "Halim Yanikomeroglu"
      ],
      "published_at": "2026-10-16T14:26:03Z",
      "updated_at": "2026-10-16T14:26:03Z",
      "abstract": "This paper introduces Quantum-Inspired Energy-Neutral (QIEN) UAV Trajectory for WPT-Enabled Industrial HRLLC, a novel framework for mobility-integrated trajectory and resource optimization in solar-powered uncrewed aerial vehicle (UAV) relay systems supporting autonomous mining robots in post-disaster environments. We formulate a mixed-integer nonlinear program (MINLP) that jointly optimizes UAV trajectory, finite blocklength allocation, transmit power, and wireless power transfer (WPT) over discrete time horizons to maximize aggregate data throughput. The system operates under strict extreme hyper-reliable low-latency communication (HRLLC) constraints, energy sustainability requirements via solar harvesting, and continuous robot power delivery. To solve this large-scale optimization problem, QIEN employs a hybrid Cooperative Quantum-behaved Particle Swarm Optimization (Co-QPSO) algorithm using quantum tunneling mechanisms and a heterogeneous swarm architecture. Our simulation results demonstrate that the proposed QIEN framework significantly outperforms benchmark algorithms, achieving successful convergence while maintaining energy-neutral operation and satisfying all reliability, power, and mobility constraints.",
      "source_url": "https://arxiv.org/abs/2610.23524",
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "제조"
      ],
      "techniques": [
        "시뮬레이션",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-20T07:44:48Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "제조": [
            "manufacturing"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "Simulation"
          ],
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-21079",
      "arxiv_id": "2610.21079",
      "title": "Reliability and Preventive Maintenance for Multivariate Gamma Degradation with Shared Random Effects and Cumulative Environmental Shock Damage",
      "summary_ko": "In industrial engineering, many highly reliable products have multiple performance characteristics that degrade simultaneously over time. Owing to common manufacturing factors and shared operating environments, their degradation processes are often statistically dependent. External environmental shocks may also accumulate into common damage and jointly contribute to system failure. To characterize such dependence, unit heterogeneity, and cumulative environmental damage, this study proposes a multivariate Gamma degradation model with a shared random effect and a shared environmental degradation process. Each degradation process consists of an individual Gamma process and a shared Gamma process, while the random effect captures latent heterogeneity across units and induces additional dependence. An explicit expression for system reliability is derived. For parameter estimation, an expectation maximization algorithm combined with variational inference is developed, and parametric bootstrap is used for uncertainty quantification. A preventive maintenance optimization approach is further established. Simulation studies and a coating degradation application demonstrate the effectiveness and practical applicability of the proposed methods.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Jiale Niu"
      ],
      "published_at": "2026-10-15T01:33:02Z",
      "updated_at": "2026-10-15T01:33:02Z",
      "abstract": "In industrial engineering, many highly reliable products have multiple performance characteristics that degrade simultaneously over time. Owing to common manufacturing factors and shared operating environments, their degradation processes are often statistically dependent. External environmental shocks may also accumulate into common damage and jointly contribute to system failure. To characterize such dependence, unit heterogeneity, and cumulative environmental damage, this study proposes a multivariate Gamma degradation model with a shared random effect and a shared environmental degradation process. Each degradation process consists of an individual Gamma process and a shared Gamma process, while the random effect captures latent heterogeneity across units and induces additional dependence. An explicit expression for system reliability is derived. For parameter estimation, an expectation maximization algorithm combined with variational inference is developed, and parametric bootstrap is used for uncertainty quantification. A preventive maintenance optimization approach is further established. Simulation studies and a coating degradation application demonstrate the effectiveness and practical applicability of the proposed methods.",
      "source_url": "https://arxiv.org/abs/2610.21079",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-20T07:44:48Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power-system"
          ]
        },
        "technique_matches": {
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-23519",
      "arxiv_id": "2610.23519",
      "title": "Diversifying Moving Target Defense Against Adaptive Attacks Through Factorized Diffusion",
      "summary_ko": "Proactive defense offers a means to enhance power-system cybersecurity through controlled modifications of system configurations. One example is moving target defense (MTD), which uses distributed flexible AC transmission system (D-FACTS) devices to perturb transmission-line reactances against stealthy false data injection attacks. Existing MTD methods primarily rely on deterministic optimization, producing a single perturbation plan for each operating condition. However, repeated or highly correlated plans may expose predictable system configurations that attackers can learn and exploit. To address this limitation, this paper reformulates MTD as a conditional generative problem that produces diverse, secure, and physically feasible perturbation candidates. Learning such a distribution is challenging because MTD decisions jointly involve selecting branches, determining perturbation magnitudes, and coordinating adjustments across the network. To make this problem tractable, we factorize the decision distribution into three sequential conditional stages: branch selection, magnitude allocation, and continuous refinement. Graph-conditioned diffusion models are employed to capture the discrete and continuous decisions while accounting for network dependencies. Furthermore, a - objective is incorporated into diffusion training to favor candidates with desirable defense effectiveness and operational costs. Numerical studies on the IEEE 118- and 300-bus systems show that the proposed framework achieves AC feasibility comparable to that of optimization-based MTD baselines and higher - values, while reducing online computation time by an order of magnitude. The generated candidate pool remains effective under changing operating conditions and device availability, providing diverse high-value alternatives for flexible online cyber defense.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Chenhan Xiao",
        "Yang Weng"
      ],
      "published_at": "2026-10-16T14:22:38Z",
      "updated_at": "2026-10-16T14:22:38Z",
      "abstract": "Proactive defense offers a means to enhance power-system cybersecurity through controlled modifications of system configurations. One example is moving target defense (MTD), which uses distributed flexible AC transmission system (D-FACTS) devices to perturb transmission-line reactances against stealthy false data injection attacks. Existing MTD methods primarily rely on deterministic optimization, producing a single perturbation plan for each operating condition. However, repeated or highly correlated plans may expose predictable system configurations that attackers can learn and exploit. To address this limitation, this paper reformulates MTD as a conditional generative problem that produces diverse, secure, and physically feasible perturbation candidates. Learning such a distribution is challenging because MTD decisions jointly involve selecting branches, determining perturbation magnitudes, and coordinating adjustments across the network. To make this problem tractable, we factorize the decision distribution into three sequential conditional stages: branch selection, magnitude allocation, and continuous refinement. Graph-conditioned diffusion models are employed to capture the discrete and continuous decisions while accounting for network dependencies. Furthermore, a - objective is incorporated into diffusion training to favor candidates with desirable defense effectiveness and operational costs. Numerical studies on the IEEE 118- and 300-bus systems show that the proposed framework achieves AC feasibility comparable to that of optimization-based MTD baselines and higher - values, while reducing online computation time by an order of magnitude. The generated candidate pool remains effective under changing operating conditions and device availability, providing diverse high-value alternatives for flexible online cyber defense.",
      "source_url": "https://arxiv.org/abs/2610.23519",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-20T07:44:48Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "도로교통": [
            "traffic-signal"
          ]
        },
        "technique_matches": {
          "최적화": [
            "Optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-19916",
      "arxiv_id": "2610.19916",
      "title": "On the Regularization of Primal-Dual Dynamics in Time-Dependent Nonconvex Optimization",
      "summary_ko": "We study continuous-time primal-dual dynamics for tracking solutions of time-dependent constrained optimization problems. First, for uniformly strongly convex-strongly concave saddle functions, we establish explicit finite-time tracking and additive-disturbance bounds. For convex problems, we then introduce a directional primal regularization, combined with dual Tikhonov regularization, and quantify the resulting optimization bias and constraint violation. For smooth nonconvex problems, we couple a proximal local convexification with a two-time-scale primal-dual flow and obtain a local tracking result under constraint qualification, second-order sufficiency, and temporal regularity. Numerical traffic-signal experiments at a single intersection evaluate the proposed method under vehicle-throughput and pedestrian-aware objectives, comparing it with standard signal-control schemes.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Miguel Vaquero",
        "Jorge Cortés"
      ],
      "published_at": "2026-10-14T10:06:44Z",
      "updated_at": "2026-10-14T10:06:44Z",
      "abstract": "We study continuous-time primal-dual dynamics for tracking solutions of time-dependent constrained optimization problems. First, for uniformly strongly convex-strongly concave saddle functions, we establish explicit finite-time tracking and additive-disturbance bounds. For convex problems, we then introduce a directional primal regularization, combined with dual Tikhonov regularization, and quantify the resulting optimization bias and constraint violation. For smooth nonconvex problems, we couple a proximal local convexification with a two-time-scale primal-dual flow and obtain a local tracking result under constraint qualification, second-order sufficiency, and temporal regularity. Numerical traffic-signal experiments at a single intersection evaluate the proposed method under vehicle-throughput and pedestrian-aware objectives, comparing it with standard signal-control schemes.",
      "source_url": "https://arxiv.org/abs/2610.19916",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-21T07:48:46Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power system"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2610-22572",
      "arxiv_id": "2610.22572",
      "title": "DC-TORQ: Fast Assessment of Data Center-Induced Generator Torsional Oscillation Risk Quantification",
      "summary_ko": "The periodic power demand of large data centers can introduce forced oscillations into the power system. When these oscillations contain frequency components near a turbine-generator torsional mode, they may excite sustained shaft torque oscillations with amplitudes exceeding the allowable cyclic torque limits. Characterizing the impact of data center power oscillations on generator shaft torque requires accounting for frequency-dependent network coupling among the data center, the transmission network, and the generators. Although electromagnetic transient (EMT) simulations can capture these interactions in detail, repeated simulations across excitation frequencies and load locations can be computationally demanding. This paper proposes an analytical framework for data center induced torsional oscillations that retains the coupled electrical and shaft dynamics. A detailed generator-network model is established, and closed-form transfer functions that map data center power perturbations to the torque in each shaft section are derived. Admissible shaft torque amplitudes are then translated into frequency-dependent limits on data center power oscillations. The proposed framework enables fast assessment of the shaft torque response while accounting for network dynamics and provides a quantitative screening criterion for candidate data center connection buses. The framework is validated on the IEEE benchmark model and applied to the IEEE 39-bus system.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Yingyi Tang",
        "Bin Wang",
        "Junbo Zhao",
        "Qiang Zhang",
        "Pradip Vijayan",
        "Xiaochuan Luo",
        "Tongxin Zheng"
      ],
      "published_at": "2026-10-15T19:16:29Z",
      "updated_at": "2026-10-15T19:16:29Z",
      "abstract": "The periodic power demand of large data centers can introduce forced oscillations into the power system. When these oscillations contain frequency components near a turbine-generator torsional mode, they may excite sustained shaft torque oscillations with amplitudes exceeding the allowable cyclic torque limits. Characterizing the impact of data center power oscillations on generator shaft torque requires accounting for frequency-dependent network coupling among the data center, the transmission network, and the generators. Although electromagnetic transient (EMT) simulations can capture these interactions in detail, repeated simulations across excitation frequencies and load locations can be computationally demanding. This paper proposes an analytical framework for data center induced torsional oscillations that retains the coupled electrical and shaft dynamics. A detailed generator-network model is established, and closed-form transfer functions that map data center power perturbations to the torque in each shaft section are derived. Admissible shaft torque amplitudes are then translated into frequency-dependent limits on data center power oscillations. The proposed framework enables fast assessment of the shaft torque response while accounting for network dynamics and provides a quantitative screening criterion for candidate data center connection buses. The framework is validated on the IEEE benchmark model and applied to the IEEE 39-bus system.",
      "source_url": "https://arxiv.org/abs/2610.22572",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "시뮬레이션",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-21T07:48:46Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "도로교통": [
            "Traffic Signal"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation",
            "simulator"
          ],
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-19022",
      "arxiv_id": "2610.19022",
      "title": "A Performative Control Approach to Day-to-Day Traffic Signal Retiming",
      "summary_ko": "Conventional fixed-time traffic signal optimization and recent day-to-day signal retiming generally model route adaptation under prescribed route-choice behavior and origin-destination (OD) demand, while neglecting the fact that travelers' behavior can be performative to the signal control policy. A signal timing policy learned from historical traffic observations must therefore account for how its own deployment changes the distribution on which it will be evaluated in the future. We formulate this problem as performative signal control under observed traveler responses and characterize performative stability of the coupled policy-demand system. We propose incremental day-to-day retiming using the local policy gradient at the current operating point, estimated offline through simulation-based zeroth-order optimization without an explicit day-to-day demand-response model. This gradual approach provides a feasible path for policy and demand to co-evolve toward stability. Experiments on a downtown Denver network show that our approach improves congestion, throughput, and stability, generalizes across different demand levels, and remains effective when the offline simulator contains mismatched travel demand and routing behaviors.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Shuchang Jiang",
        "Xinwu Qian",
        "Santiago Segarra",
        "Zhongyuan Zhao"
      ],
      "published_at": "2026-10-13T18:16:16Z",
      "updated_at": "2026-10-13T18:16:16Z",
      "abstract": "Conventional fixed-time traffic signal optimization and recent day-to-day signal retiming generally model route adaptation under prescribed route-choice behavior and origin-destination (OD) demand, while neglecting the fact that travelers' behavior can be performative to the signal control policy. A signal timing policy learned from historical traffic observations must therefore account for how its own deployment changes the distribution on which it will be evaluated in the future. We formulate this problem as performative signal control under observed traveler responses and characterize performative stability of the coupled policy-demand system. We propose incremental day-to-day retiming using the local policy gradient at the current operating point, estimated offline through simulation-based zeroth-order optimization without an explicit day-to-day demand-response model. This gradual approach provides a feasible path for policy and demand to co-evolve toward stability. Experiments on a downtown Denver network show that our approach improves congestion, throughput, and stability, generalizes across different demand levels, and remains effective when the offline simulator contains mismatched travel demand and routing behaviors.",
      "source_url": "https://arxiv.org/abs/2610.19022",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "철도"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-21T07:48:46Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "철도": [
            "railway"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2610-06073",
      "arxiv_id": "2610.06073",
      "title": "On the Sum of Inverse Gamma RVs and its Application to Wireless Communications Networks",
      "summary_ko": "Investigating the statistics of the sum of random variables (RVs) is fundamental in wireless communication systems, as it can be used to accurately estimate their performance. In this paper, exact closed-form expressions are derived for the probability density function (PDF) and cumulative distribution function (CDF) of the sum of inverse-gamma (IG) RVs. The IG distribution is especially suitable for performance analysis purposes because it provides a tractable yet accurate model of shadowing/local mean power variations. Unlike prior studies on sums of shadowing RVs, the resulting formulas are simple to evaluate and numerically stable. Their utility is demonstrated in two practical settings: unmanned aerial vehicle (UAV)-assisted networks and high-speed railway communications. Across all considered scenarios, the analytical results are in excellent agreement with the Monte Carlo simulation results, while the asymptotic approximations closely track the exact results",
      "doi": "10.1109/TVT.2026.3702729",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Petros S. Bithas",
        "Liang Yang",
        "Qiuming Zhu",
        "Hector E. Nistazakis",
        "Daniel Benevides Da Costa"
      ],
      "published_at": "2026-10-05T10:05:57Z",
      "updated_at": "2026-10-05T10:05:57Z",
      "abstract": "Investigating the statistics of the sum of random variables (RVs) is fundamental in wireless communication systems, as it can be used to accurately estimate their performance. In this paper, exact closed-form expressions are derived for the probability density function (PDF) and cumulative distribution function (CDF) of the sum of inverse-gamma (IG) RVs. The IG distribution is especially suitable for performance analysis purposes because it provides a tractable yet accurate model of shadowing/local mean power variations. Unlike prior studies on sums of shadowing RVs, the resulting formulas are simple to evaluate and numerically stable. Their utility is demonstrated in two practical settings: unmanned aerial vehicle (UAV)-assisted networks and high-speed railway communications. Across all considered scenarios, the analytical results are in excellent agreement with the Monte Carlo simulation results, while the asymptotic approximations closely track the exact results",
      "source_url": "https://arxiv.org/abs/2610.06073",
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇",
        "도로교통"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-21T07:48:46Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robots"
          ],
          "도로교통": [
            "traffic-signal",
            "road network"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2609-39637",
      "arxiv_id": "2609.39637",
      "title": "Prediction is Better than Detection: Traffic Congestion Control using Drones",
      "summary_ko": "A central question in deploying teams of mobile robots for persistent monitoring is how task performance scales with fleet size, and whether this scaling holds once sensing drives downstream action rather than mere observation. We study this question for a team of drones performing traffic-jam detection and prediction in a simulated road network, whose reports drive an adaptive traffic-signal controller in closed loop. We build a multi-agent simulation, with vehicles following Nagel-Schreckenberg cellular-automaton dynamics and drones patrolling junctions via a round-robin policy, and sweep fleet size, traffic level, and network size to evaluate detection rate, detection delay, and prediction rate. We show how performance plateaus for fleet size approximating the number of junctions being monitored, and offer a general fleet-provisioning rule for persistent-monitoring deployments. More significantly, adapting the signal on a predicted jam, rather than a detected one, roughly doubles the resulting reduction in jam duration, showing that the value of onboard prediction in a sensing-to-action pipeline can exceed the value of adding more robots. Prediction accuracy, not sensing coverage, is now the binding constraint on further improvement, pointing to onboard inference, not fleet size, as the more promising direction for future work.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Samira Hayat",
        "Christian Raffelsberger"
      ],
      "published_at": "2026-09-30T12:42:16Z",
      "updated_at": "2026-09-30T12:42:16Z",
      "abstract": "A central question in deploying teams of mobile robots for persistent monitoring is how task performance scales with fleet size, and whether this scaling holds once sensing drives downstream action rather than mere observation. We study this question for a team of drones performing traffic-jam detection and prediction in a simulated road network, whose reports drive an adaptive traffic-signal controller in closed loop. We build a multi-agent simulation, with vehicles following Nagel-Schreckenberg cellular-automaton dynamics and drones patrolling junctions via a round-robin policy, and sweep fleet size, traffic level, and network size to evaluate detection rate, detection delay, and prediction rate. We show how performance plateaus for fleet size approximating the number of junctions being monitored, and offer a general fleet-provisioning rule for persistent-monitoring deployments. More significantly, adapting the signal on a predicted jam, rather than a detected one, roughly doubles the resulting reduction in jam duration, showing that the value of onboard prediction in a sensing-to-action pipeline can exceed the value of adding more robots. Prediction accuracy, not sensing coverage, is now the binding constraint on further improvement, pointing to onboard inference, not fleet size, as the more promising direction for future work.",
      "source_url": "https://arxiv.org/abs/2609.39637",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "건설"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-21T07:48:46Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "건설": [
            "Construction Site"
          ]
        },
        "technique_matches": {
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-22929",
      "arxiv_id": "2610.22929",
      "title": "DICL-Qwen3VL: Decoupled Confidence Calibration for Interpretable Workwear Detection",
      "summary_ko": "Poor illumination and occlusion obscure clothing cues in indoor work sites, while diverse clothing styles complicate workwear compliance judgments. Although vision-language models (VLMs) support localization and semantic reasoning, their predictions often lack explicit supporting evidence, and a single confidence score cannot distinguish visual uncertainty from ambiguity in compliance judgments. We propose DICL-Qwen3VL, a decoupled interpretable VLM that integrates structured workwear detection, evidence generation, and separate visual and reasoning confidence estimates. First, low-rank adaptation (LoRA) adapts Qwen3-VL to localize visible upper-body clothing and identify workwear. Second, decoupled calibration separates visual observations from compliance reasoning and assigns a confidence score to each. Token entropy and sensitivity to image perturbations provide supervision for visual confidence, linking confidence estimation to the generated observations and their dependence on image content. Finally, group relative policy optimization (GRPO) jointly optimizes detection accuracy, output validity, and confidence calibration. Experiments on the company-collected Indoor Construction Site Workwear Dataset (ICSWD) and the web-collected Indoor Construction Workwear Dataset (WICWD) yield mAP@50 values of 0.730 and 0.753, respectively, outperforming the evaluated LoRA-adapted VLM baselines, including Qwen3-VL. The framework also provides explicit evidence and separate confidence scores to support manual review in industrial safety monitoring. WICWD is publicly available at https://github.com/DaErwen1/WICWD.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Meng Wang",
        "Wu Zeng",
        "Mei Li",
        "Chao He"
      ],
      "published_at": "2026-10-16T04:46:20Z",
      "updated_at": "2026-10-16T04:46:20Z",
      "abstract": "Poor illumination and occlusion obscure clothing cues in indoor work sites, while diverse clothing styles complicate workwear compliance judgments. Although vision-language models (VLMs) support localization and semantic reasoning, their predictions often lack explicit supporting evidence, and a single confidence score cannot distinguish visual uncertainty from ambiguity in compliance judgments. We propose DICL-Qwen3VL, a decoupled interpretable VLM that integrates structured workwear detection, evidence generation, and separate visual and reasoning confidence estimates. First, low-rank adaptation (LoRA) adapts Qwen3-VL to localize visible upper-body clothing and identify workwear. Second, decoupled calibration separates visual observations from compliance reasoning and assigns a confidence score to each. Token entropy and sensitivity to image perturbations provide supervision for visual confidence, linking confidence estimation to the generated observations and their dependence on image content. Finally, group relative policy optimization (GRPO) jointly optimizes detection accuracy, output validity, and confidence calibration. Experiments on the company-collected Indoor Construction Site Workwear Dataset (ICSWD) and the web-collected Indoor Construction Workwear Dataset (WICWD) yield mAP@50 values of 0.730 and 0.753, respectively, outperforming the evaluated LoRA-adapted VLM baselines, including Qwen3-VL. The framework also provides explicit evidence and separate confidence scores to support manual review in industrial safety monitoring. WICWD is publicly available at https://github.com/DaErwen1/WICWD.",
      "source_url": "https://arxiv.org/abs/2610.22929",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "우주",
        "로봇"
      ],
      "techniques": [
        "디지털트윈"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-21T07:48:46Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "우주": [
            "satellite"
          ],
          "로봇": [
            "robot"
          ]
        },
        "technique_matches": {
          "디지털트윈": [
            "Digital Twin",
            "digital-twin"
          ]
        }
      },
      "id": "auto-arxiv-2610-26201",
      "arxiv_id": "2610.26201",
      "title": "K-AoI: A Spatio-Temporal Synchronization Metric for Digital Twin-based Teleoperation in Dynamic Network Environments",
      "summary_ko": "Synchronization between a digital twin (DT) and its physical robot is critical for effective teleoperation, as mismatches directly degrade operator awareness and task performance. Existing synchronization metrics are predominantly time-based and rarely capture the spatial consequences of delayed information during motion. In this paper, we introduce Kinematic Age of Information (K-AoI), a spatio-temporal metric that weights AoI by instantaneous Cartesian velocity, translating temporal staleness into physically meaningful spatial divergence. We implement and evaluate K-AoI on a DT teleoperation testbed comprising a six-degrees-of-freedom (6-DOF) manipulator, a Unity-based twin, and controlled network impairments using tc netem across seven network conditions. Per-sample analysis shows that K-AoI exhibits rank-correlations with AoI at $ρ\\leq 0.65$ across all networks, dropping to $ρ= 0.22$ under satellite connections. This shows that K-AoI is not a simple transformation of AoI, but instead captures velocity-dependent degradation effects that time-only metrics cannot resolve. We further analyze AoI tail behavior using Ultra-Low AoI (ULAoI) and find that the commonly assumed Generalized Pareto Distribution (GPD) model is rejected. This reflects the impact of deterministic forwarding and bounded jitter, which violate standard extreme-value assumptions. Overall, K-AoI provides a complementary and physically grounded synchronization metric, linking network delay to task-relevant spatial error in digital-twin teleoperation systems. The dataset and testbed software are publicly available at: https://github.com/luziyaotian/kaoi-testbed.",
      "first_seen_at": "2026-10-20T07:44:48Z",
      "discovery_sources": [],
      "authors": [
        "Lu Tian",
        "Zhenhui Yuan",
        "Zexin Deng",
        "Wenjuan Zhang"
      ],
      "published_at": "2026-10-19T11:29:06Z",
      "updated_at": "2026-10-19T11:29:06Z",
      "abstract": "Synchronization between a digital twin (DT) and its physical robot is critical for effective teleoperation, as mismatches directly degrade operator awareness and task performance. Existing synchronization metrics are predominantly time-based and rarely capture the spatial consequences of delayed information during motion. In this paper, we introduce Kinematic Age of Information (K-AoI), a spatio-temporal metric that weights AoI by instantaneous Cartesian velocity, translating temporal staleness into physically meaningful spatial divergence. We implement and evaluate K-AoI on a DT teleoperation testbed comprising a six-degrees-of-freedom (6-DOF) manipulator, a Unity-based twin, and controlled network impairments using tc netem across seven network conditions. Per-sample analysis shows that K-AoI exhibits rank-correlations with AoI at $ρ\\leq 0.65$ across all networks, dropping to $ρ= 0.22$ under satellite connections. This shows that K-AoI is not a simple transformation of AoI, but instead captures velocity-dependent degradation effects that time-only metrics cannot resolve. We further analyze AoI tail behavior using Ultra-Low AoI (ULAoI) and find that the commonly assumed Generalized Pareto Distribution (GPD) model is rejected. This reflects the impact of deterministic forwarding and bounded jitter, which violate standard extreme-value assumptions. Overall, K-AoI provides a complementary and physically grounded synchronization metric, linking network delay to task-relevant spatial error in digital-twin teleoperation systems. The dataset and testbed software are publicly available at: https://github.com/luziyaotian/kaoi-testbed.",
      "source_url": "https://arxiv.org/abs/2610.26201",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "시뮬레이션",
        "데이터 분석"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-22T07:44:50Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "energy systems"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulations",
            "simulator"
          ],
          "데이터 분석": [
            "time-series"
          ]
        }
      },
      "id": "auto-arxiv-2610-20978",
      "arxiv_id": "2610.20978",
      "title": "A differentiable PCA-GPR surrogate framework for full-transient Bayesian inverse uncertainty quantification of black-box nuclear thermal-hydraulic systems",
      "summary_ko": "Complex transient energy systems require uncertainty-quantified predictions, but Bayesian inverse uncertainty quantification (IUQ) is often impractical because posterior sampling with high-fidelity system codes requires many expensive full-transient simulations. This study develops a surrogate-assisted Bayesian IUQ framework that makes full-time-series calibration practical for the NACIE heavy-liquid-metal natural-circulation loop. The key innovation is to transform a black-box transient simulator into a differentiable probabilistic emulator by combining principal component analysis with Gaussian process regression (PCA--GPR). The surrogate reconstructs complete multichannel temperature and mass-flow histories from a compact latent representation, preserving dominant temporal and cross-channel correlations while enabling automatic differentiation of the log posterior. This allows both gradient-free ensemble slice sampling and gradient-informed sampling with the No-U-Turn Sampler and Microcanonical Langevin Monte Carlo. Five principal components capture more than 99\\% of the output variance, and the surrogate achieves normalized root-mean-square errors below 2\\%. Compared with direct Flownex SE evaluations, the surrogate reduces likelihood-evaluation cost by approximately 3000-fold while preserving posterior statistics and correlations. Posterior predictive analyses show reduced bias, narrower intervals, and improved agreement with measurements and independent validation.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Zikang Yaoa",
        "Eugene Yeea",
        "Fue-Sang Lien"
      ],
      "published_at": "2026-10-14T21:48:34Z",
      "updated_at": "2026-10-14T21:48:34Z",
      "abstract": "Complex transient energy systems require uncertainty-quantified predictions, but Bayesian inverse uncertainty quantification (IUQ) is often impractical because posterior sampling with high-fidelity system codes requires many expensive full-transient simulations. This study develops a surrogate-assisted Bayesian IUQ framework that makes full-time-series calibration practical for the NACIE heavy-liquid-metal natural-circulation loop. The key innovation is to transform a black-box transient simulator into a differentiable probabilistic emulator by combining principal component analysis with Gaussian process regression (PCA--GPR). The surrogate reconstructs complete multichannel temperature and mass-flow histories from a compact latent representation, preserving dominant temporal and cross-channel correlations while enabling automatic differentiation of the log posterior. This allows both gradient-free ensemble slice sampling and gradient-informed sampling with the No-U-Turn Sampler and Microcanonical Langevin Monte Carlo. Five principal components capture more than 99\\% of the output variance, and the surrogate achieves normalized root-mean-square errors below 2\\%. Compared with direct Flownex SE evaluations, the surrogate reduces likelihood-evaluation cost by approximately 3000-fold while preserving posterior statistics and correlations. Posterior predictive analyses show reduced bias, narrower intervals, and improved agreement with measurements and independent validation.",
      "source_url": "https://arxiv.org/abs/2610.20978",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "AI",
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-22T07:44:50Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "도로교통": [
            "Traffic Signal"
          ]
        },
        "technique_matches": {
          "AI": [
            "reinforcement learning"
          ],
          "시뮬레이션": [
            "Simulation"
          ]
        }
      },
      "id": "auto-arxiv-2610-09563",
      "arxiv_id": "2610.09563",
      "title": "EvoSignal: LLM-Guided Evolutionary Design of Modular Traffic Signal Control Programs",
      "summary_ko": "Effective traffic signal control (TSC) requires policies that respond to changing traffic demand and network conditions while meeting different control objectives. However, adapting existing strategies often involves repeated manual design and adjustment, making it difficult to systematically explore better control rules for a target network. Large language models (LLMs) can automate this process, but directly using them to select signal phases leaves decision rules embedded in black-box models and incurs recurring inference costs and latency. This paper formulates TSC as a modular program design problem and proposes EvoSignal, an LLM-guided evolutionary framework using traffic knowledge and performance feedback. The modular representation separates traffic feature extraction, local phase prioritization, and optional network-based priority adjustment. Starting from several established strategies, EvoSignal improves programs through feedback on congestion and signal operation, retaining strategies with different performance trade-offs. The resulting programs operate without online LLM inference. Simulation experiments across five scenarios on two real-world road networks show that the selected default EvoSignal program reduces waiting time by 16.8--49.2\\% relative to the lowest waiting time achieved by the 20 conventional, reinforcement learning-based, and LLM-based baselines in each scenario. A program prioritizing travel time and queue length outperforms all 20 baselines on all three metrics in the search scenario and remains among the top three on each metric when transferred unchanged to the other four scenarios. These findings support automated design of inspectable control programs that transfer across the evaluated road networks and traffic demands. Code is available at https://github.com/georgewanglz2019/EvoSignal.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Leizhen Wang",
        "Peibo Duan",
        "Zhenlin Qin",
        "Yancheng Ling",
        "Jian Xu",
        "Yue Wang",
        "Hao Wang",
        "Zhenliang Ma"
      ],
      "published_at": "2026-10-07T07:04:22Z",
      "updated_at": "2026-10-11T08:28:40Z",
      "abstract": "Effective traffic signal control (TSC) requires policies that respond to changing traffic demand and network conditions while meeting different control objectives. However, adapting existing strategies often involves repeated manual design and adjustment, making it difficult to systematically explore better control rules for a target network. Large language models (LLMs) can automate this process, but directly using them to select signal phases leaves decision rules embedded in black-box models and incurs recurring inference costs and latency. This paper formulates TSC as a modular program design problem and proposes EvoSignal, an LLM-guided evolutionary framework using traffic knowledge and performance feedback. The modular representation separates traffic feature extraction, local phase prioritization, and optional network-based priority adjustment. Starting from several established strategies, EvoSignal improves programs through feedback on congestion and signal operation, retaining strategies with different performance trade-offs. The resulting programs operate without online LLM inference. Simulation experiments across five scenarios on two real-world road networks show that the selected default EvoSignal program reduces waiting time by 16.8--49.2\\% relative to the lowest waiting time achieved by the 20 conventional, reinforcement learning-based, and LLM-based baselines in each scenario. A program prioritizing travel time and queue length outperforms all 20 baselines on all three metrics in the search scenario and remains among the top three on each metric when transferred unchanged to the other four scenarios. These findings support automated design of inspectable control programs that transfer across the evaluated road networks and traffic demands. Code is available at https://github.com/georgewanglz2019/EvoSignal.",
      "source_url": "https://arxiv.org/abs/2610.09563",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "철도"
      ],
      "techniques": [
        "AI",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-22T07:44:50Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "철도": [
            "Railway"
          ]
        },
        "technique_matches": {
          "AI": [
            "Deep Learning"
          ],
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-00363",
      "arxiv_id": "2610.00363",
      "title": "Deep Learning for Anomaly Detection in Railway Systems: A Structured Survey",
      "summary_ko": "Ensuring safe and reliable operation of modern railway systems increasingly relies on data-driven monitoring and intelligent fault detection. Deep learning has emerged as an effective paradigm for railway anomaly detection, driven by the growing availability of heterogeneous sensor data from rolling stock and infrastructure. This paper presents a structured survey of deep learning-based anomaly detection approaches for railway systems. The surveyed methods are organized using a unified taxonomy covering anomaly location, data representation and manifestation, sensing modality, and temporal characteristics. Existing approaches, including convolutional, recurrent and attention-based architectures, autoencoders, generative adversarial networks, and transformers, are structured into classification-based, prediction-based, reconstruction-based, and hybrid learning paradigms. The survey also examines data-centric challenges, evaluation practices, performance metrics, and practical deployment aspects, including edge-cloud architectures, computational constraints, and hardware-aware optimization. Finally, a decision-oriented framework links anomaly characteristics, data properties, and operational constraints to suitable detection paradigms and deployment configurations. This work provides a structured reference for selecting and deploying deep learning solutions for railway anomaly detection and highlights open challenges toward reliable and scalable intelligent monitoring systems.",
      "doi": "10.1016/j.engappai.2026.115776",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Ammar Bouketta",
        "Smail Niar",
        "Hamza Ouarnoughi"
      ],
      "published_at": "2026-09-30T06:29:59Z",
      "updated_at": "2026-09-30T06:29:59Z",
      "abstract": "Ensuring safe and reliable operation of modern railway systems increasingly relies on data-driven monitoring and intelligent fault detection. Deep learning has emerged as an effective paradigm for railway anomaly detection, driven by the growing availability of heterogeneous sensor data from rolling stock and infrastructure. This paper presents a structured survey of deep learning-based anomaly detection approaches for railway systems. The surveyed methods are organized using a unified taxonomy covering anomaly location, data representation and manifestation, sensing modality, and temporal characteristics. Existing approaches, including convolutional, recurrent and attention-based architectures, autoencoders, generative adversarial networks, and transformers, are structured into classification-based, prediction-based, reconstruction-based, and hybrid learning paradigms. The survey also examines data-centric challenges, evaluation practices, performance metrics, and practical deployment aspects, including edge-cloud architectures, computational constraints, and hardware-aware optimization. Finally, a decision-oriented framework links anomaly characteristics, data properties, and operational constraints to suitable detection paradigms and deployment configurations. This work provides a structured reference for selecting and deploying deep learning solutions for railway anomaly detection and highlights open challenges toward reliable and scalable intelligent monitoring systems.",
      "source_url": "https://arxiv.org/abs/2610.00363",
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-22T07:44:50Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robot",
            "robots",
            "robotics"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulating"
          ]
        }
      },
      "id": "auto-arxiv-2609-31357",
      "arxiv_id": "2609.31357",
      "title": "Transformer-based Monte Carlo Localization in Construction Meshes",
      "summary_ko": "To be able to perform inspection or digitization tasks, mobile robots on construction sites must be able to localize themselves reliably with respect to a global reference frame that is shared with a building map. Similar room layouts and low-texture surfaces pose a challenge for existing LiDAR- and vision-based localization methods. We approach this problem with a LiDAR-based global relocalization system that estimates the robot's pose relative to a building mesh and combines a PointNet++ encoder with a place recognition decoder, whose outputs serve as a learned observation model within a Monte Carlo Localization (MCL) framework. The pipeline is trained exclusively on synthetic LiDAR scans obtained by simulating the robot's sensors inside the building mesh. Our approach is robust in ambiguous environments due to an uncertainty-aware decoder that scales positional likelihoods and a resampling strategy that injects model hypotheses into the particle set, enabling recovery from potential particle depletion. Evaluations on real-world datasets show that our method outperforms both diffusion-based and ScanContext++ baselines while maintaining fast inference (18 ms per call), demonstrating the practicality of synthetic-data training for mesh-referenced global localization in construction robotics.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Linus Kramer",
        "William Talbot",
        "Olga Vysotska",
        "Marco Hutter"
      ],
      "published_at": "2026-09-25T14:54:15Z",
      "updated_at": "2026-09-25T14:54:15Z",
      "abstract": "To be able to perform inspection or digitization tasks, mobile robots on construction sites must be able to localize themselves reliably with respect to a global reference frame that is shared with a building map. Similar room layouts and low-texture surfaces pose a challenge for existing LiDAR- and vision-based localization methods. We approach this problem with a LiDAR-based global relocalization system that estimates the robot's pose relative to a building mesh and combines a PointNet++ encoder with a place recognition decoder, whose outputs serve as a learned observation model within a Monte Carlo Localization (MCL) framework. The pipeline is trained exclusively on synthetic LiDAR scans obtained by simulating the robot's sensors inside the building mesh. Our approach is robust in ambiguous environments due to an uncertainty-aware decoder that scales positional likelihoods and a resampling strategy that injects model hypotheses into the particle set, enabling recovery from potential particle depletion. Evaluations on real-world datasets show that our method outperforms both diffusion-based and ScanContext++ baselines while maintaining fast inference (18 ms per call), demonstrating the practicality of synthetic-data training for mesh-referenced global localization in construction robotics.",
      "source_url": "https://arxiv.org/abs/2609.31357",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "제조"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-22T07:44:50Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "제조": [
            "manufacturing"
          ]
        },
        "technique_matches": {
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-25565",
      "arxiv_id": "2610.25565",
      "title": "Concurrent two-scale topology optimization of printable bone scaffolds via rectified-flow generation and exact latent-space refinement",
      "summary_ko": "Bone scaffolds must reconcile a macroscale mechanical response with microscale geometric feasibility, yet conventional approaches to their design restrict attainable constitutive properties. This work presents a concurrent two-scale topology optimization framework; a conditional rectified flow generates periodic lattice microstructures, which are then refined in latent space by exact finite-element homogenization sensitivities. The resulting unit-cell geometries and their spatial assignments are jointly optimized under mechanical and feature-size constraints. In a distal rabbit femur benchmark, all eight flow-started refinements produce printable cells with maximum relative errors under 1\\% across six prescribed engineering moduli. Constructing the implant from a single tiled cell reproduces intact whole-femur compliance within 0.1\\% and regional strain energy within 0.35\\% under four loading conditions. In human femoral reconstruction, multiscale concurrent optimization produces a cortical mid-shaft scaffold with porosity 63\\% while satisfying the compliance constraint within 2\\% under walking and stair-climbing loads. It achieves an 84\\% porosity on a proximal cancellous scaffold with strain-energy deviations below 0.25\\% under the same loads. Analysis of the learned representations reveals distinct architectural families in latent space. Further, the attention heads of the rectified-flow transformer specialize for spatial shifts -- learning the analogue of a finite-difference stencil -- and coordinate symmetries, with ablations showing their influence on generated topologies. These results establish a route to printable, mechanically constrained scaffold designs. The framework is general enough for extending to many other multiscale structural design applications including topology-microstructure co-design in metal additive manufacturing.",
      "first_seen_at": "2026-10-20T07:44:48Z",
      "discovery_sources": [],
      "authors": [
        "Reza T Batley",
        "Sourav Saha"
      ],
      "published_at": "2026-10-18T22:00:02Z",
      "updated_at": "2026-10-18T22:00:02Z",
      "abstract": "Bone scaffolds must reconcile a macroscale mechanical response with microscale geometric feasibility, yet conventional approaches to their design restrict attainable constitutive properties. This work presents a concurrent two-scale topology optimization framework; a conditional rectified flow generates periodic lattice microstructures, which are then refined in latent space by exact finite-element homogenization sensitivities. The resulting unit-cell geometries and their spatial assignments are jointly optimized under mechanical and feature-size constraints. In a distal rabbit femur benchmark, all eight flow-started refinements produce printable cells with maximum relative errors under 1\\% across six prescribed engineering moduli. Constructing the implant from a single tiled cell reproduces intact whole-femur compliance within 0.1\\% and regional strain energy within 0.35\\% under four loading conditions. In human femoral reconstruction, multiscale concurrent optimization produces a cortical mid-shaft scaffold with porosity 63\\% while satisfying the compliance constraint within 2\\% under walking and stair-climbing loads. It achieves an 84\\% porosity on a proximal cancellous scaffold with strain-energy deviations below 0.25\\% under the same loads. Analysis of the learned representations reveals distinct architectural families in latent space. Further, the attention heads of the rectified-flow transformer specialize for spatial shifts -- learning the analogue of a finite-difference stencil -- and coordinate symmetries, with ablations showing their influence on generated topologies. These results establish a route to printable, mechanically constrained scaffold designs. The framework is general enough for extending to many other multiscale structural design applications including topology-microstructure co-design in metal additive manufacturing.",
      "source_url": "https://arxiv.org/abs/2610.25565",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "로봇",
        "물류"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-22T07:44:50Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robot",
            "robots"
          ],
          "물류": [
            "logistics"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "Simulation"
          ]
        }
      },
      "id": "auto-arxiv-2610-24549",
      "arxiv_id": "2610.24549",
      "title": "CARVS: A Congestion-Aware Algorithm for Online Multi-Agent Pickup-and-Delivery in Human-Collaborative Warehouses",
      "summary_ko": "Intelligent warehouse systems have transformed logistics by integrating robots with minimal infrastructure changes, boosting efficiency and optimizing workflows compared to traditional human-only operations. However, human-robot collaboration introduces new challenges, particularly due to human uncertainty, which leads to dynamic interactions and unpredictable behavior. To address these complexities, this paper presents the Multi-Agent Pickup-and-Delivery with Human Collaboration (MAPDwHC) problem, an extension of the Multi-Agent Pickup-and-Delivery (MAPD) problem that incorporates human unpredictability. This problem adds key constraints, such as dynamic obstacles caused by robots temporarily blocking paths when interacting with humans, and the need for real-time responsiveness. We formalize the MAPDwHC problem with additional constraints and properties. Based on this problem setting, we propose the CARVS algorithm, which improves task assignment and path planning by integrating task swapping, validation checks, and random initialization. CARVS reduces congestion, enhances adaptability to human-robot interactions, and ensures real-time responsiveness. Simulation results show that CARVS significantly reduces timeouts of path planning in large-scale scenarios where traditional MAPD algorithms fail, improving both efficiency and scalability.",
      "first_seen_at": "2026-10-20T07:44:48Z",
      "discovery_sources": [],
      "authors": [
        "Chaoran Li",
        "Zeyuan Zhao",
        "Shao Zhang"
      ],
      "published_at": "2026-10-17T08:17:52Z",
      "updated_at": "2026-10-17T08:17:52Z",
      "abstract": "Intelligent warehouse systems have transformed logistics by integrating robots with minimal infrastructure changes, boosting efficiency and optimizing workflows compared to traditional human-only operations. However, human-robot collaboration introduces new challenges, particularly due to human uncertainty, which leads to dynamic interactions and unpredictable behavior. To address these complexities, this paper presents the Multi-Agent Pickup-and-Delivery with Human Collaboration (MAPDwHC) problem, an extension of the Multi-Agent Pickup-and-Delivery (MAPD) problem that incorporates human unpredictability. This problem adds key constraints, such as dynamic obstacles caused by robots temporarily blocking paths when interacting with humans, and the need for real-time responsiveness. We formalize the MAPDwHC problem with additional constraints and properties. Based on this problem setting, we propose the CARVS algorithm, which improves task assignment and path planning by integrating task swapping, validation checks, and random initialization. CARVS reduces congestion, enhances adaptability to human-robot interactions, and ensures real-time responsiveness. Simulation results show that CARVS significantly reduces timeouts of path planning in large-scale scenarios where traditional MAPD algorithms fail, improving both efficiency and scalability.",
      "source_url": "https://arxiv.org/abs/2610.24549",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-23T07:42:21Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power systems"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2610-19918",
      "arxiv_id": "2610.19918",
      "title": "On the Mechanism of Multi-Cluster System Separation Under Extreme Events",
      "summary_ko": "Historical blackout events demonstrate that, under complex multi-event contingencies, interconnected power systems can split into multiple areas exhibiting distinct dynamic behavior while remaining physically connected. This work provides a rationale for this phenomenon. Typical N-1 contingency analysis leads to a response dominated by a single unstable mode, supporting the two-cluster conjecture used by transient stability assessment tools such as the SIME method. However, extreme events, often involving cascading N-k contingencies, can excite multiple unstable modes. We establish a proof of concept based on linear modal expansion to show how such concurrent excitation leads to multiple coherent areas. The resulting behavior is illustrated through simulations on the standard WSCC 9-bus, IEEE 14-bus, and IEEE 39-bus systems, demonstrating the limitations of conventional two-cluster representations under extreme events.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Nikolaos Sofos",
        "Georgios Tzounas",
        "Federico Milano"
      ],
      "published_at": "2026-10-14T10:09:19Z",
      "updated_at": "2026-10-14T10:09:19Z",
      "abstract": "Historical blackout events demonstrate that, under complex multi-event contingencies, interconnected power systems can split into multiple areas exhibiting distinct dynamic behavior while remaining physically connected. This work provides a rationale for this phenomenon. Typical N-1 contingency analysis leads to a response dominated by a single unstable mode, supporting the two-cluster conjecture used by transient stability assessment tools such as the SIME method. However, extreme events, often involving cascading N-k contingencies, can excite multiple unstable modes. We establish a proof of concept based on linear modal expansion to show how such concurrent excitation leads to multiple coherent areas. The resulting behavior is illustrated through simulations on the standard WSCC 9-bus, IEEE 14-bus, and IEEE 39-bus systems, demonstrating the limitations of conventional two-cluster representations under extreme events.",
      "source_url": "https://arxiv.org/abs/2610.19918",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-23T07:42:21Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "도로교통": [
            "Traffic Signal",
            "traffic signals"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2609-38280",
      "arxiv_id": "2609.38280",
      "title": "Timing Sensitivity in Actuated Traffic Signal Control: A simulation study on one urban network",
      "summary_ko": "A comparison between adaptive and fixed-time traffic signals can change when only their timing settings change. We examine this sensitivity in 240 simulation runs on one OpenStreetMap-derived urban network, using eight configurations, three arrival rates and ten paired demand seeds. The experiment varies the maximum green of presence-based actuation, retimes the fixed baseline, and includes a discharge-based phase-termination rule. The endpoint is finite-window time spent per offered trip, including entry waiting and penalties for removed or abandoned trips. At the highest tested load, actuation with a maximum green of twice the planned split has a mean endpoint 57.4 per cent above the original fixed plan. Reducing that multiplier to 1.25 puts it 70.7 per cent below the same plan. Against the best of three tested fixed plans, discharge-based termination reduces the mean endpoint by 14.8, 15.8 and 18.3 per cent across the three loads. Both timing sweeps favour their shortest tested setting, so neither identifies an optimum. This exploratory study uses synthetic demand, negligible start-up lost time and physical removal of persistently stationary vehicles. The results support testing timing sensitivity before attributing performance to a controller family; they do not establish a setting for real-world deployment.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Nitai Aharoni"
      ],
      "published_at": "2026-09-29T16:01:21Z",
      "updated_at": "2026-09-29T16:01:21Z",
      "abstract": "A comparison between adaptive and fixed-time traffic signals can change when only their timing settings change. We examine this sensitivity in 240 simulation runs on one OpenStreetMap-derived urban network, using eight configurations, three arrival rates and ten paired demand seeds. The experiment varies the maximum green of presence-based actuation, retimes the fixed baseline, and includes a discharge-based phase-termination rule. The endpoint is finite-window time spent per offered trip, including entry waiting and penalties for removed or abandoned trips. At the highest tested load, actuation with a maximum green of twice the planned split has a mean endpoint 57.4 per cent above the original fixed plan. Reducing that multiplier to 1.25 puts it 70.7 per cent below the same plan. Against the best of three tested fixed plans, discharge-based termination reduces the mean endpoint by 14.8, 15.8 and 18.3 per cent across the three loads. Both timing sweeps favour their shortest tested setting, so neither identifies an optimum. This exploratory study uses synthetic demand, negligible start-up lost time and physical removal of persistently stationary vehicles. The results support testing timing sensitivity before attributing performance to a controller family; they do not establish a setting for real-world deployment.",
      "source_url": "https://arxiv.org/abs/2609.38280",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "철도"
      ],
      "techniques": [
        "AI"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-23T07:42:21Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "철도": [
            "Railway",
            "Railways"
          ]
        },
        "technique_matches": {
          "AI": [
            "Reinforcement Learning",
            "neural networks"
          ]
        }
      },
      "id": "auto-arxiv-2609-30150",
      "arxiv_id": "2609.30150",
      "title": "Graph-Based Inference and Topology-Aware Multi-Agent Reinforcement Learning for Large-Scale Railway Network Management",
      "summary_ko": "Modern infrastructure asset management constitutes a complex sequential decision-making problem, characterized by long planning horizons and system-level interactions, such as spatial deterioration correlations and economies of scale. While deep reinforcement learning has shown promise in optimizing maintenance policies, scaling to real-world networks remains challenging. Centralized approaches become computationally intractable in large-scale systems, whereas decentralized approaches often fail to capture essential coordination mechanisms. To address these challenges, we propose a graph-based framework that integrates accurate environment modeling with scalable decision support. First, we employ a hierarchical Bayesian model leveraging a Gaussian Process on Graph kernel to infer a realistic, spatially correlated networked environment of railway maintenance planning from real-world data provided by the Swiss Federal Railways. Second, we introduce a topology-aware Multi-Agent Reinforcement Learning (MARL) framework by integrating graph neural networks and graph Transformers to optimize network-level policies. A central contribution of this work is the demonstration of scalability through zero-shot transfer learning: graph-based agents, trained only on small network portions, are successfully deployed in a zero-shot manner on large-scale unseen networks without any retraining. Numerical results indicate that the proposed method significantly outperforms optimized heuristics and standard MARL baselines, reducing computational training time while maintaining superior performance on large-scale networks.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Giacomo Arcieri",
        "Gregory Duthé",
        "Christophe Muller",
        "Konstantinos G. Papakonstantinou",
        "Daniel Straub",
        "Eleni Chatzi"
      ],
      "published_at": "2026-09-24T17:12:39Z",
      "updated_at": "2026-09-24T17:12:39Z",
      "abstract": "Modern infrastructure asset management constitutes a complex sequential decision-making problem, characterized by long planning horizons and system-level interactions, such as spatial deterioration correlations and economies of scale. While deep reinforcement learning has shown promise in optimizing maintenance policies, scaling to real-world networks remains challenging. Centralized approaches become computationally intractable in large-scale systems, whereas decentralized approaches often fail to capture essential coordination mechanisms. To address these challenges, we propose a graph-based framework that integrates accurate environment modeling with scalable decision support. First, we employ a hierarchical Bayesian model leveraging a Gaussian Process on Graph kernel to infer a realistic, spatially correlated networked environment of railway maintenance planning from real-world data provided by the Swiss Federal Railways. Second, we introduce a topology-aware Multi-Agent Reinforcement Learning (MARL) framework by integrating graph neural networks and graph Transformers to optimize network-level policies. A central contribution of this work is the demonstration of scalability through zero-shot transfer learning: graph-based agents, trained only on small network portions, are successfully deployed in a zero-shot manner on large-scale unseen networks without any retraining. Numerical results indicate that the proposed method significantly outperforms optimized heuristics and standard MARL baselines, reducing computational training time while maintaining superior performance on large-scale networks.",
      "source_url": "https://arxiv.org/abs/2609.30150",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "AI",
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-23T07:42:21Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robot",
            "Robotic"
          ]
        },
        "technique_matches": {
          "AI": [
            "reinforcement learning"
          ],
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2609-20477",
      "arxiv_id": "2609.20477",
      "title": "Visual Sim-to-Real Learning for Robotic Insertion under Geometric Variations: Application to Rebar Installation",
      "summary_ko": "Rebar insertion is among the most repetitive and physically demanding tasks on construction sites, and a contact-rich problem at 1.4 mm clearance. The parts, however, vary at two levels: a nominal design per structural member, and fabrication tolerance around each nominal design. Real-world data therefore has to be re-collected as designs and batches change. We present RebarSim, a visual sim-to-real system trained entirely in simulation. A privileged state-based teacher is trained with reinforcement learning over procedurally generated rebar geometries, then distilled into a multi-view student that maps raw RGB and proprioception directly to actions under extensive domain randomization. The student transfers to the real world zero-shot, seating rebars taken from a real factory production run in 91.3% of real-robot rollouts. Underlying that result, geometry diversity and pretraining both bring benefits. Training across a diverse set of nominal designs rather than one lifts the zero-shot success of both the teacher and the student on unseen designs, and the student policy outperforms a single-design specialist on that specialist's own design. A pretrained student then adapts to a new design with 4--6x fewer distillation samples than one trained from scratch. Visual sim-to-real transfer depends on appearance randomization and the DAgger mixture: removing either one sharply lowers success. Videos, code, and task assets are available at https://rebarsim.github.io.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Tao Sun",
        "Beining Han",
        "Patrick Yin",
        "Rui Xu",
        "Harry He",
        "Abhishek Gupta",
        "Szymon Rusinkiewicz",
        "Yi Shao"
      ],
      "published_at": "2026-09-17T14:32:30Z",
      "updated_at": "2026-09-17T14:32:30Z",
      "abstract": "Rebar insertion is among the most repetitive and physically demanding tasks on construction sites, and a contact-rich problem at 1.4 mm clearance. The parts, however, vary at two levels: a nominal design per structural member, and fabrication tolerance around each nominal design. Real-world data therefore has to be re-collected as designs and batches change. We present RebarSim, a visual sim-to-real system trained entirely in simulation. A privileged state-based teacher is trained with reinforcement learning over procedurally generated rebar geometries, then distilled into a multi-view student that maps raw RGB and proprioception directly to actions under extensive domain randomization. The student transfers to the real world zero-shot, seating rebars taken from a real factory production run in 91.3% of real-robot rollouts. Underlying that result, geometry diversity and pretraining both bring benefits. Training across a diverse set of nominal designs rather than one lifts the zero-shot success of both the teacher and the student on unseen designs, and the student policy outperforms a single-design specialist on that specialist's own design. A pretrained student then adapts to a new design with 4--6x fewer distillation samples than one trained from scratch. Visual sim-to-real transfer depends on appearance randomization and the DAgger mixture: removing either one sharply lowers success. Videos, code, and task assets are available at https://rebarsim.github.io.",
      "source_url": "https://arxiv.org/abs/2609.20477",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "제조"
      ],
      "techniques": [
        "AI",
        "시뮬레이션",
        "디지털트윈"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-23T07:42:21Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "제조": [
            "manufacturing"
          ]
        },
        "technique_matches": {
          "AI": [
            "machine-learning"
          ],
          "시뮬레이션": [
            "simulation"
          ],
          "디지털트윈": [
            "Digital Twin"
          ]
        }
      },
      "id": "auto-arxiv-2610-28050",
      "arxiv_id": "2610.28050",
      "title": "A Process-Informed, Photorealistic Digital Twin for Vision-Based Inline Inspection of C-SMC Strip Deposition",
      "summary_ko": "Inline quality assurance for Sheet Molding Compound (SMC) manufacturing is hindered by the lack of large-scale, accurately labeled image datasets for training machine-learning models. This work presents a process-informed, photorealistic digital twin for carbon-fiber SMC strip deposition. A rigid-body simulation reproduces the deposition kinematics (settling, friction, belt motion), while a physically grounded polarization-rendering model produces realistic appearance, so that every image carries exact, mutually consistent labels in synchronized formats: oriented bounding boxes, point annotations, density maps, and polarization renderings (Angle and Degree of Linear Polarization). Because the joint distribution of many overlapping strips cannot be specified analytically, simulation yields exact, scalable ground truth that manual annotation cannot provide. The twin produces over 16,000 images across 3,300 frames, and the synthetic data replicate near-isotropic fiber orientation (axial concentration r < 0.05) consistent with as-deposited commercial SMC. Trained purely on synthetic data, YOLOv11 and CSRNet reach detection mAP50 up to 0.91, with a dedicated oriented-bounding-box detector confirming the oriented labels are directly trainable (mAP50 = 0.83); on real manufacturing images, the synthetic-to-real difference in edge and texture structure (Kolmogorov-Smirnov distance 0.46, distribution overlap 0.56) is of the same magnitude as the difference between two real acquisition series of the same material (0.41, overlap 0.57). The approach substantially reduces the labeling burden for camera-based inline inspection of fiber orientation and volume content.",
      "first_seen_at": "2026-10-21T07:48:46Z",
      "discovery_sources": [],
      "authors": [
        "Patrick Flore",
        "Fabian Röder",
        "Kevin Chen",
        "Andreas Gebhard"
      ],
      "published_at": "2026-10-20T11:38:13Z",
      "updated_at": "2026-10-20T11:38:13Z",
      "abstract": "Inline quality assurance for Sheet Molding Compound (SMC) manufacturing is hindered by the lack of large-scale, accurately labeled image datasets for training machine-learning models. This work presents a process-informed, photorealistic digital twin for carbon-fiber SMC strip deposition. A rigid-body simulation reproduces the deposition kinematics (settling, friction, belt motion), while a physically grounded polarization-rendering model produces realistic appearance, so that every image carries exact, mutually consistent labels in synchronized formats: oriented bounding boxes, point annotations, density maps, and polarization renderings (Angle and Degree of Linear Polarization). Because the joint distribution of many overlapping strips cannot be specified analytically, simulation yields exact, scalable ground truth that manual annotation cannot provide. The twin produces over 16,000 images across 3,300 frames, and the synthetic data replicate near-isotropic fiber orientation (axial concentration r < 0.05) consistent with as-deposited commercial SMC. Trained purely on synthetic data, YOLOv11 and CSRNet reach detection mAP50 up to 0.91, with a dedicated oriented-bounding-box detector confirming the oriented labels are directly trainable (mAP50 = 0.83); on real manufacturing images, the synthetic-to-real difference in edge and texture structure (Kolmogorov-Smirnov distance 0.46, distribution overlap 0.56) is of the same magnitude as the difference between two real acquisition series of the same material (0.41, overlap 0.57). The approach substantially reduces the labeling burden for camera-based inline inspection of fiber orientation and volume content.",
      "source_url": "https://arxiv.org/abs/2610.28050",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "로봇",
        "제조",
        "물류"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-23T07:42:21Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robot"
          ],
          "제조": [
            "manufacturing"
          ],
          "물류": [
            "logistics"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2610-29964",
      "arxiv_id": "2610.29964",
      "title": "Object- and Task-Aware Active Perception for Safe Navigation of Mobile Manipulators in Semi-Static Environments",
      "summary_ko": "Mobile manipulators must navigate safely in cluttered, partially unknown environments across logistics, manufacturing, and service applications. Semi-static changes between robot visits can lead to outdated scene knowledge and increased collision risk. We propose an active perception framework that enables a high degree-of-freedom (DoF) mobile manipulator to inspect uncertain or safety-critical regions with its hand camera during navigation. A probabilistic, object- and task-aware next-best-view (NBV) formulation prioritizes observations near the desired path and around uncertain or potentially changing objects. The perception objective is integrated into hierarchical-task model predictive control (HTMPC), exploiting kinematic redundancy for viewpoint adjustment while preserving the higher priority of base-trajectory tracking. The system unifies navigation, map maintenance, and active perception in a closed loop. Real-world tests show improved map consistency, reduced task-dependent map entropy, and lower navigation risk than passive or non-task-aware baselines. Additional simulation and real-world sequences demonstrate responses to controlled semi-static scene changes.",
      "first_seen_at": "2026-10-22T07:44:50Z",
      "discovery_sources": [],
      "authors": [
        "Jingxing Qian",
        "Xintong Du",
        "Jun Yang",
        "Siqi Zhou",
        "Steven L. Waslander",
        "Angela P. Schoellig"
      ],
      "published_at": "2026-10-21T16:19:15Z",
      "updated_at": "2026-10-21T16:19:15Z",
      "abstract": "Mobile manipulators must navigate safely in cluttered, partially unknown environments across logistics, manufacturing, and service applications. Semi-static changes between robot visits can lead to outdated scene knowledge and increased collision risk. We propose an active perception framework that enables a high degree-of-freedom (DoF) mobile manipulator to inspect uncertain or safety-critical regions with its hand camera during navigation. A probabilistic, object- and task-aware next-best-view (NBV) formulation prioritizes observations near the desired path and around uncertain or potentially changing objects. The perception objective is integrated into hierarchical-task model predictive control (HTMPC), exploiting kinematic redundancy for viewpoint adjustment while preserving the higher priority of base-trajectory tracking. The system unifies navigation, map maintenance, and active perception in a closed loop. Real-world tests show improved map consistency, reduced task-dependent map entropy, and lower navigation risk than passive or non-task-aware baselines. Additional simulation and real-world sequences demonstrate responses to controlled semi-static scene changes.",
      "source_url": "https://arxiv.org/abs/2610.29964",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "AI",
        "디지털트윈",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-24T07:25:16Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robot",
            "robots"
          ]
        },
        "technique_matches": {
          "AI": [
            "AI"
          ],
          "디지털트윈": [
            "digital twin"
          ],
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-23826",
      "arxiv_id": "2610.23826",
      "title": "Self-Building Labs",
      "summary_ko": "Self-driving laboratories (SDLs) accelerate scientific discovery by automating experimental execution and optimization. AI agents can increasingly propose new experiments, but existing SDLs can only run the procedures they were built for. Adapting them to a new experiment still takes extensive manual engineering, which makes laboratory flexibility the bottleneck for scaling discovery. We introduce the self-building laboratory (SBL), which designs, configures, and operates its own laboratory setup given a high-level scientific goal. SBL starts from whatever already exists, from an empty room to a working SDL, and builds the missing capabilities: the lab layout, its digital twin, robot skills, and experimental workflows. We demonstrate the system working across different robots and setups, scaling from a single workstation to laboratories with several robots working together. We validate SBL's end-to-end capabilities through a real-world quantum-dot synthesis experiment. By shifting the role of human researchers from manually configuring and programming device automation to specifying scientific objectives, SBL makes scientific automation more accessible, flexible, and scalable. Project website: https://self-building-labs.github.io/",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Jiaru Bai",
        "Yuchi Zhao",
        "Han Hao",
        "Elia Savino",
        "Miroslav Bogdanovic",
        "Derk H. W. ten Klooster",
        "Simone Pilon",
        "Timothy Noël",
        "Yang Cao",
        "Varinia Bernales",
        "Alán Aspuru-Guzik"
      ],
      "published_at": "2026-10-16T17:48:29Z",
      "updated_at": "2026-10-30T17:54:41Z",
      "abstract": "Self-driving laboratories (SDLs) accelerate scientific discovery by automating experimental execution and optimization. AI agents can increasingly propose new experiments, but existing SDLs can only run the procedures they were built for. Adapting them to a new experiment still requires extensive manual engineering, which makes laboratory flexibility the bottleneck for scaling discovery. We introduce the self-building laboratory (SBL), which designs, configures, and operates its own laboratory setup given a high-level scientific goal. SBL starts from whatever already exists, from an empty room to a working SDL, and builds the missing capabilities: the lab layout, its digital twin, robot skills, and experimental workflows. We demonstrate the system working across different robots and setups, scaling from a single workstation to laboratories with several robots working together. We validate SBL's end-to-end capabilities through a real-world quantum-dot synthesis experiment. By shifting human researchers from manually configuring and programming device automation to specifying scientific objectives, SBL makes scientific automation more accessible, flexible, and scalable. Project website: https://self-building-labs.github.io/",
      "source_url": "https://arxiv.org/abs/2610.23826",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "AI",
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-24T07:25:16Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power-system"
          ]
        },
        "technique_matches": {
          "AI": [
            "AI"
          ],
          "시뮬레이션": [
            "simulation",
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2610-19565",
      "arxiv_id": "2610.19565",
      "title": "Identifiability-Guided Composite Load Modeling of AI Data Centers",
      "summary_ko": "The rapid growth of AI data centers introduces large, workload-dependent loads whose protection and reconnection behavior can affect power-system transient stability. Calibrating dynamic models of such loads is challenging because treating all associated elements as freely estimable may lead to weakly identified or physically confounded parameterizations. This paper proposes an identifiability-guided calibration framework for a composite load model with separate IT, cooling, and auxiliary branches. A nuisance-projected Fisher-information criterion characterizes the local identifiability of the cooling-motor composition, while a workload non-equivalence result shows that voltage-only recalibration cannot reproduce workload-dependent responses. Guided by these results, a grey-box estimator trained with branch-level supervision learns the workload-to-power relation and the cooling composition, while retaining a prescribed UPS protection law and an externally anchored reconnect-ramp duration. The learned workload relation is then distilled into a closed form for transient-stability simulation. Evaluation using 300,000 MIT Supercloud GPU power-utilization samples and 12,600 IEEE 39-bus fault scenarios yields an aggregate active-power $R^2$ of 0.948, compared with 0.856 for an LSTM baseline. Removing branch-level supervision preserves relatively high aggregate accuracy but substantially degrades cooling-component recovery. Under unseen deep voltage sags, the proposed model achieves a transient $R^2$ of 0.750, compared with 0.033 for the LSTM. Closed-loop simulations further show that cooling composition and protection behavior can affect retained-load states and voltage responses near protection thresholds.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Ding Lin",
        "Jianhui Wang",
        "Meng Yue",
        "Tianqiao Zhao"
      ],
      "published_at": "2026-10-14T04:28:41Z",
      "updated_at": "2026-10-17T22:57:50Z",
      "abstract": "The rapid growth of AI data centers introduces large, workload-dependent loads whose protection and reconnection behavior can affect power-system transient stability. Calibrating dynamic models of such loads is challenging because treating all associated elements as freely estimable may lead to weakly identified or physically confounded parameterizations. This paper proposes an identifiability-guided calibration framework for a composite load model with separate IT, cooling, and auxiliary branches. A nuisance-projected Fisher-information criterion characterizes the local identifiability of the cooling-motor composition, while a workload non-equivalence result shows that voltage-only recalibration cannot reproduce workload-dependent responses. Guided by these results, a grey-box estimator trained with branch-level supervision learns the workload-to-power relation and the cooling composition, while retaining a prescribed UPS protection law and an externally anchored reconnect-ramp duration. The learned workload relation is then distilled into a closed form for transient-stability simulation. Evaluation using 300,000 MIT Supercloud GPU power-utilization samples and 12,600 IEEE 39-bus fault scenarios yields an aggregate active-power $R^2$ of 0.948, compared with 0.856 for an LSTM baseline. Removing branch-level supervision preserves relatively high aggregate accuracy but substantially degrades cooling-component recovery. Under unseen deep voltage sags, the proposed model achieves a transient $R^2$ of 0.750, compared with 0.033 for the LSTM. Closed-loop simulations further show that cooling composition and protection behavior can affect retained-load states and voltage responses near protection thresholds.",
      "source_url": "https://arxiv.org/abs/2610.19565",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "AI",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-24T07:25:16Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "도로교통": [
            "Traffic Signal",
            "traffic-flow"
          ]
        },
        "technique_matches": {
          "AI": [
            "reinforcement learning"
          ],
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2609-36934",
      "arxiv_id": "2609.36934",
      "title": "VLALight: A Vision-Language-Action Model for Traffic Signal Control",
      "summary_ko": "Traffic signal control (TSC) is essential for improving urban mobility and reducing congestion. Although roadside cameras are widely deployed at signalized intersections and provide rich visual observations of evolving traffic, existing TSC methods typically rely on manually engineered traffic states or separate perception modules, creating a gap between physical observations and control decisions. We present VLALight, the first vision-language-action (VLA) model for end-to-end traffic signal control from multi-view roadside videos. VLALight directly maps visual observations to coordinated signal actions through multi-target spatiotemporal traffic reasoning and topology-aware cooperative perception across intersections. To establish this capability, we develop a two-stage supervised cold-start training strategy for visual traffic understanding and signal decision-making, followed by cooperative agentic reinforcement learning that jointly optimizes local control and network-wide traffic efficiency. Furthermore, VLALight introduces adaptive fast and slow reasoning modes, enabling the policy to allocate deeper reasoning only when additional deliberation provides sufficient control benefits. Through balanced mode-aware rollouts and relative advantage optimization, VLALight learns to trade off decision quality and inference cost. Extensive experiments on seven real-world traffic-flow datasets across three urban networks demonstrate that VLALight consistently outperforms transportation-based, RL-based, and LLM/VLM-based baselines. Ablation studies validate the effectiveness of cooperative perception, network-level optimization, and adaptive reasoning. These results demonstrate the potential of VLA models for real-world physical traffic control. Our project is available at https://github.com/usail-hkust/VLALight.git.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Pan Zhang",
        "Siqi Lai",
        "Kemu Dong",
        "Hao Liu"
      ],
      "published_at": "2026-09-29T07:48:08Z",
      "updated_at": "2026-09-29T07:48:08Z",
      "abstract": "Traffic signal control (TSC) is essential for improving urban mobility and reducing congestion. Although roadside cameras are widely deployed at signalized intersections and provide rich visual observations of evolving traffic, existing TSC methods typically rely on manually engineered traffic states or separate perception modules, creating a gap between physical observations and control decisions. We present VLALight, the first vision-language-action (VLA) model for end-to-end traffic signal control from multi-view roadside videos. VLALight directly maps visual observations to coordinated signal actions through multi-target spatiotemporal traffic reasoning and topology-aware cooperative perception across intersections. To establish this capability, we develop a two-stage supervised cold-start training strategy for visual traffic understanding and signal decision-making, followed by cooperative agentic reinforcement learning that jointly optimizes local control and network-wide traffic efficiency. Furthermore, VLALight introduces adaptive fast and slow reasoning modes, enabling the policy to allocate deeper reasoning only when additional deliberation provides sufficient control benefits. Through balanced mode-aware rollouts and relative advantage optimization, VLALight learns to trade off decision quality and inference cost. Extensive experiments on seven real-world traffic-flow datasets across three urban networks demonstrate that VLALight consistently outperforms transportation-based, RL-based, and LLM/VLM-based baselines. Ablation studies validate the effectiveness of cooperative perception, network-level optimization, and adaptive reasoning. These results demonstrate the potential of VLA models for real-world physical traffic control. Our project is available at https://github.com/usail-hkust/VLALight.git.",
      "source_url": "https://arxiv.org/abs/2609.36934",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지",
        "철도"
      ],
      "techniques": [
        "시뮬레이션",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-24T07:25:16Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "Power Systems",
            "renewable energy"
          ],
          "철도": [
            "Railway"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulations"
          ],
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2609-21808",
      "arxiv_id": "2609.21808",
      "title": "Distributed Intraday Energy Management for 16.7-Hz Railway Power Systems---Part I: Theoretical Foundations",
      "summary_ko": "In a series of three papers, we propose a distributed intraday scenario planner for energy management in 16.7-Hz single-phase railway power systems, which is realized and tested as open-source Python package \\texttt{bahnstrom\\_ems v0.9.1}~\\cite{noroozi2026bahnstromems}. The first part of our work develops the mathematical foundation of a two-layer energy management system for a 16.7-Hz single-phase railway power network, where the network is an interconnection of several control areas. Given a day-ahead plan, last measurement of stored energy in wayside batteries, and forecasts of the tractive, available regenerative and renewable energy, we provide a mathematical modeling of the 16.7-Hz single-phase railway power network in intraday time-scale. Then the intraday energy planning is formulated as a risk-neutral convex stochastic optimization problem. The overall intraday planning is shown to be a sparse convex quadratic program. Hence, this problem is solved recursively by adopting a distributed scenario model predictive control (MPC) setting. The corresponding area problems, coordination updates, convergence conditions, and closed-loop key-performance-index accumulation are derived. In practice, detailed train-motion and microscopic motoring and regenerative-power profiles are usually needed for accurate simulations and numerical validation purposes. Due to data privacy and security concerns, detailed train-motion and microscopic motoring and regenerative-power profiles in a real power network may not be publicly available. Therefore, we also propose a mathematically-solid approach to estimate such microscopic information by converting causally timetable and train-motion information into quarter-hour motoring and regenerative-power profiles while exactly preserving each hourly energy target.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Navid Noroozi"
      ],
      "published_at": "2026-09-18T14:18:55Z",
      "updated_at": "2026-09-18T14:18:55Z",
      "abstract": "In a series of three papers, we propose a distributed intraday scenario planner for energy management in 16.7-Hz single-phase railway power systems, which is realized and tested as open-source Python package \\texttt{bahnstrom\\_ems v0.9.1}~\\cite{noroozi2026bahnstromems}. The first part of our work develops the mathematical foundation of a two-layer energy management system for a 16.7-Hz single-phase railway power network, where the network is an interconnection of several control areas. Given a day-ahead plan, last measurement of stored energy in wayside batteries, and forecasts of the tractive, available regenerative and renewable energy, we provide a mathematical modeling of the 16.7-Hz single-phase railway power network in intraday time-scale. Then the intraday energy planning is formulated as a risk-neutral convex stochastic optimization problem. The overall intraday planning is shown to be a sparse convex quadratic program. Hence, this problem is solved recursively by adopting a distributed scenario model predictive control (MPC) setting. The corresponding area problems, coordination updates, convergence conditions, and closed-loop key-performance-index accumulation are derived. In practice, detailed train-motion and microscopic motoring and regenerative-power profiles are usually needed for accurate simulations and numerical validation purposes. Due to data privacy and security concerns, detailed train-motion and microscopic motoring and regenerative-power profiles in a real power network may not be publicly available. Therefore, we also propose a mathematically-solid approach to estimate such microscopic information by converting causally timetable and train-motion information into quarter-hour motoring and regenerative-power profiles while exactly preserving each hourly energy target.",
      "source_url": "https://arxiv.org/abs/2609.21808",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "제조"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-24T07:25:16Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "제조": [
            "manufacturing"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2610-27825",
      "arxiv_id": "2610.27825",
      "title": "Early-stage local melting during solid-state sintering of a Cu-Ni-Ti-Zr-Hf alloy: A combined thermodynamic and kinetic analysis",
      "summary_ko": "Complex concentrated alloys (CCAs) combining elements from 3d transition and refractory groups, specifically Cu-Ni-Ti-Zr-Hf-based shape memory systems, exhibit excellent functional properties but present significant manufacturing challenges via powder metallurgy. In this study, a non-equimolar Cu15Ni35Ti25Zr12.5Hf12.5 (at. %) alloy was successfully produced by induction melting, followed by atomization and subsequent consolidation via Spark Plasma Sintering (SPS) at temperatures ranging from 850 to 1100 C. Although global equilibrium phase diagrams predict solid-state stability up to 1260 C, localized melting unexpectedly occurred during SPS at 1100 C. By combining retrospective Scheil-Gulliver solidification simulations with 2D local equilibrium heat maps, we demonstrate that this early-stage melting is not driven by macroscopic thermal gradients, but by microsegregation inherited from atomization. During solid-state heating, thermally activated short-range diffusion, accelerated by high defect densities at particle interfaces, partitions Ti towards the interparticle network, leaving Cu-enriched regions (up to ~41 at. %) within the particle interiors. Owing to the strongly negative mixing enthalpy of Cu with Hf (-19.2 kJ/mol for $ΔH_{mix}^{l-l}$) and, to a lesser extent, with Ti, the local solidus of these Cu-rich regions is dramatically depressed. Upon heating to 1100 C, these regions cross their local solidus and form a liquid phase, whose solidification produces the Cu-Hf co-segregation. This framework accounts for localized melting phenomena in rapidly solidified CCA powders containing low-melting constituents with strong negative mixing enthalpies.",
      "first_seen_at": "2026-10-21T07:48:46Z",
      "discovery_sources": [],
      "authors": [
        "Andrea Skolakova",
        "Hana Lesakova",
        "Frantisek Lukac",
        "Marketa Jarosova",
        "Jan Pinc"
      ],
      "published_at": "2026-10-20T08:15:14Z",
      "updated_at": "2026-10-30T10:44:19Z",
      "abstract": "Complex concentrated alloys (CCAs) combining elements from 3d transition and refractory groups, specifically Cu-Ni-Ti-Zr-Hf-based shape memory systems, exhibit excellent functional properties but present significant manufacturing challenges via powder metallurgy. In this study, a non-equimolar Cu15Ni35Ti25Zr12.5Hf12.5 (at. %) alloy was successfully produced by induction melting, followed by atomization and subsequent consolidation via Spark Plasma Sintering (SPS) at temperatures ranging from 850 to 1100 °C. Although equilibrium calculations using the current TCHEA7 database predict solid-state stability of the nominal composition up to slightly below 1100 °C, localized melting unexpectedly occurred during SPS already at 1000 °C and at 1100 °C. By combining retrospective Scheil-Gulliver solidification simulations with 2D local equilibrium heat maps, we demonstrate that this early-stage melting is not driven by macroscopic thermal gradients, but by microsegregation inherited from atomization. During solid-state heating, thermally activated short-range diffusion, accelerated by high defect densities at particle interfaces, partitions Ti towards the interparticle network, leaving Cu-enriched regions (up to ~41 at. %) within the particle interiors. Owing to the strongly negative mixing enthalpy of Cu with Hf (-19.2 kJ/mol for $ΔH_{mix}^{l-l}$) and, to a lesser extent, with Ti, the local solidus of these Cu-rich regions is dramatically depressed. Upon heating to 1100 °C, these regions cross their local solidus and form a liquid phase, whose solidification produces the Cu-Hf co-segregation. This framework accounts for localized melting phenomena in rapidly solidified CCA powders containing low-melting constituents with strong negative mixing enthalpies.",
      "source_url": "https://arxiv.org/abs/2610.27825",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "우주"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-24T07:25:16Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "우주": [
            "satellite",
            "satellites"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "Simulations"
          ]
        }
      },
      "id": "auto-arxiv-2610-30923",
      "arxiv_id": "2610.30923",
      "title": "Low-Latency MoE Inference in Space Data Centers: A Wireless-Power-Transfer Perspective",
      "summary_ko": "Space data centers (SpaceDCs) are emerging as platforms for large language models (LLMs), supported by continuous solar-energy harvesting and high-speed interconnections. The mixture-of-experts (MoE) architecture supports distributed LLM inference by deploying experts across satellites. However, skewed expert activation creates uneven computation power demands despite uniform solar-power supply at the dawn-dusk sun-synchronous orbit (SSO). Consequently, active experts remain power-constrained while harvested power at idle experts is underused. These reduce inference efficiency and increase latency. To address this supply-demand mismatch, we propose the SpaceWPT framework, which uses inter-satellite laser-based wireless power transfer (WPT) to redistribute harvested power. The key design challenge is to balance the computation demands of power-donating and power-receiving satellites under WPT losses: transferring more power accelerates computation at the receiver but leaves less power for the donor. After deriving design principles that simplify WPT control, we characterize the resulting latency tradeoff through a tractable surrogate of average inference latency. Minimizing the surrogate yields a novel two-sided water-filling strategy that determines whether each active expert donates, retains, or receives power according to the water level and its WPT link efficiency. Simulations with Mixtral-8x7B and Phi-3.5-MoE show up to 29.25% lower inference latency than without WPT, with larger gains under more compact formations and higher transfer efficiencies.",
      "first_seen_at": "2026-10-23T07:42:21Z",
      "discovery_sources": [],
      "authors": [
        "Haotian Zheng",
        "Zhanwei Wang",
        "Kaibin Huang"
      ],
      "published_at": "2026-10-22T08:42:28Z",
      "updated_at": "2026-10-22T08:42:28Z",
      "abstract": "Space data centers (SpaceDCs) are emerging as platforms for large language models (LLMs), supported by continuous solar-energy harvesting and high-speed interconnections. The mixture-of-experts (MoE) architecture supports distributed LLM inference by deploying experts across satellites. However, skewed expert activation creates uneven computation power demands despite uniform solar-power supply at the dawn-dusk sun-synchronous orbit (SSO). Consequently, active experts remain power-constrained while harvested power at idle experts is underused. These reduce inference efficiency and increase latency. To address this supply-demand mismatch, we propose the SpaceWPT framework, which uses inter-satellite laser-based wireless power transfer (WPT) to redistribute harvested power. The key design challenge is to balance the computation demands of power-donating and power-receiving satellites under WPT losses: transferring more power accelerates computation at the receiver but leaves less power for the donor. After deriving design principles that simplify WPT control, we characterize the resulting latency tradeoff through a tractable surrogate of average inference latency. Minimizing the surrogate yields a novel two-sided water-filling strategy that determines whether each active expert donates, retains, or receives power according to the water level and its WPT link efficiency. Simulations with Mixtral-8x7B and Phi-3.5-MoE show up to 29.25% lower inference latency than without WPT, with larger gains under more compact formations and higher transfer efficiencies.",
      "source_url": "https://arxiv.org/abs/2610.30923",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-25T07:40:43Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robots"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2610-23018",
      "arxiv_id": "2610.23018",
      "title": "VLA-Fabric: Learning Interaction for Composable Multi-Arm VLA Agents",
      "summary_ko": "Vision-language-action (VLA) models are rapidly expanding what individual robots can accomplish, yet many real-world manipulation settings require multiple arms to operate as a coordinated system. Existing approaches organize multi-arm coordination in different ways, including centralized joint policies, high-level task or role assignment, and locally executed policies that react to teammates. However, how complete action-producing VLAs should explicitly interact with one another during action generation remains largely unexplored. We introduce VLA-Fabric, which organizes one complete VLA per arm while preserving local perception and action ownership, and enables coordination through learned interaction across policy boundaries. We first systematically study where such interaction should occur, how peer information should be aggregated, and how the interaction should be learned, revealing complementary coordination functions spanning shared context, peer-specific latent information, and action-stage interaction. We then re-instantiate these functions in a structurally different VLA architecture, showing that the coordination principle is not tied to a particular tensor layout or backbone implementation. Finally, we compose teams of three and four arms and evaluate them in simulation and physical experiments. On all four physical tasks, Full interaction improves success over independently trained local policies by 16--40 percentage points. Together, these results suggest that explicit interaction among complete VLA policies offers a scalable path toward networked embodied intelligence.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Chenglong Zhang",
        "Shuaijun Liu",
        "Feiyang You",
        "Shuyang Hao",
        "Chengyu Wu",
        "Ningxin Su"
      ],
      "published_at": "2026-10-16T06:52:37Z",
      "updated_at": "2026-10-16T06:52:37Z",
      "abstract": "Vision-language-action (VLA) models are rapidly expanding what individual robots can accomplish, yet many real-world manipulation settings require multiple arms to operate as a coordinated system. Existing approaches organize multi-arm coordination in different ways, including centralized joint policies, high-level task or role assignment, and locally executed policies that react to teammates. However, how complete action-producing VLAs should explicitly interact with one another during action generation remains largely unexplored. We introduce VLA-Fabric, which organizes one complete VLA per arm while preserving local perception and action ownership, and enables coordination through learned interaction across policy boundaries. We first systematically study where such interaction should occur, how peer information should be aggregated, and how the interaction should be learned, revealing complementary coordination functions spanning shared context, peer-specific latent information, and action-stage interaction. We then re-instantiate these functions in a structurally different VLA architecture, showing that the coordination principle is not tied to a particular tensor layout or backbone implementation. Finally, we compose teams of three and four arms and evaluate them in simulation and physical experiments. On all four physical tasks, Full interaction improves success over independently trained local policies by 16--40 percentage points. Together, these results suggest that explicit interaction among complete VLA policies offers a scalable path toward networked embodied intelligence.",
      "source_url": "https://arxiv.org/abs/2610.23018",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "우주"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-25T07:40:43Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "우주": [
            "satellite"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2610-22860",
      "arxiv_id": "2610.22860",
      "title": "$\\texttt{GASTRO-BH}$: Effects of a Central Black Hole in GSE-like Dwarf-galaxy Mergers Around Milky Way-like Hosts",
      "summary_ko": "Recent evidence points to an intermediate-mass black hole (IMBH) in the most massive globular cluster of the Milky Way, $ω$ Centauri. This globular cluster is commonly associated with the Gaia-Sausage/Enceladus (GSE) dwarf galaxy, the last major merger event of our Galaxy, as its nuclear star cluster remnant. Motivated by this, we investigate whether the interaction between a bright GSE-like dwarf galaxy with stellar mass in the range $10^{8.5}$--$10^{9.7}$ solar masses and a Milky Way-like host can either suppress or enhance the growth of a central black hole (BH) during the merger process. To this end, we perform high-resolution $N$-body and smoothed particle hydrodynamics (SPH) simulations with the ChaNGa framework and a modified, angular-momentum-aware Bondi-Hoyle accretion prescription, considering both isolated dwarf galaxies and merger simulations with different stellar feedback efficiencies. The merger simulations show that the interaction does not halt BH accretion; instead, tidal interactions and gas inflows during pericentric passages strongly enhance accretion, leading to BH mass growth by more than two orders of magnitude in both low- and high-feedback models. The enhanced accretion is accompanied by suppressed star formation in the dwarf galaxy. Our results suggest that mergers similar to the GSE event can efficiently fuel IMBH growth in satellite galaxies rather than suppress it. This may be in tension with the estimated low mass of the proposed IMBH in $ω$ Centauri, but it could provide a mechanism for triggering active galactic nucleus activity in dwarf galaxies.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Pedro Naethe Motta",
        "João A. S. Amarante",
        "Guilherme Limberg",
        "Lais Borbolato",
        "Chervin F. P. Laporte",
        "Zhao-Yu Li",
        "Reinaldo Santos-Lima",
        "Victor P. Debattista",
        "Marcelo C. Vicentin"
      ],
      "published_at": "2026-10-16T03:06:24Z",
      "updated_at": "2026-10-16T03:06:24Z",
      "abstract": "Recent evidence points to an intermediate-mass black hole (IMBH) in the most massive globular cluster of the Milky Way, $ω$ Centauri. This globular cluster is commonly associated with the Gaia-Sausage/Enceladus (GSE) dwarf galaxy, the last major merger event of our Galaxy, as its nuclear star cluster remnant. Motivated by this, we investigate whether the interaction between a bright GSE-like dwarf galaxy with stellar mass in the range $10^{8.5}$--$10^{9.7}$ solar masses and a Milky Way-like host can either suppress or enhance the growth of a central black hole (BH) during the merger process. To this end, we perform high-resolution $N$-body and smoothed particle hydrodynamics (SPH) simulations with the ChaNGa framework and a modified, angular-momentum-aware Bondi-Hoyle accretion prescription, considering both isolated dwarf galaxies and merger simulations with different stellar feedback efficiencies. The merger simulations show that the interaction does not halt BH accretion; instead, tidal interactions and gas inflows during pericentric passages strongly enhance accretion, leading to BH mass growth by more than two orders of magnitude in both low- and high-feedback models. The enhanced accretion is accompanied by suppressed star formation in the dwarf galaxy. Our results suggest that mergers similar to the GSE event can efficiently fuel IMBH growth in satellite galaxies rather than suppress it. This may be in tension with the estimated low mass of the proposed IMBH in $ω$ Centauri, but it could provide a mechanism for triggering active galactic nucleus activity in dwarf galaxies.",
      "source_url": "https://arxiv.org/abs/2610.22860",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-25T07:40:43Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power systems",
            "renewable energy",
            "photovoltaic"
          ]
        },
        "technique_matches": {
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-18290",
      "arxiv_id": "2610.18290",
      "title": "On Over-Conservatism Under Low-Probability Extreme Events: A KL-Regularized Wasserstein Distributionally Robust Planning Method",
      "summary_ko": "The increasing penetration of renewable energy makes power systems more vulnerable to low-probability extreme events, elevating system planning to a critical role in ensuring reliability. Distributionally robust optimization (DRO) based on the Wasserstein distance is widely used to address this challenge. However, we find that the conventional Wasserstein ambiguity set tends to excessively amplify the probabilities of low-probability extreme events, leading to over-conservative investment decisions. To mitigate this over-conservatism, this paper proposes a novel ambiguity set that modifies the Wasserstein distance with a Kullback-Leibler (KL) divergence regularization term. By leveraging the KL divergence's sensitivity to relative probability changes, the proposed set effectively curbs the unrealistic magnification of low-probability events. A two-stage distributionally robust planning model built upon this new ambiguity set is developed to optimize the installed capacity of photovoltaic, wind, and energy storage. The complex model is then reformulated as an equivalent exponential cone program with strict theoretical proofs, making it computationally tractable. Numerical results on the PJM 5-bus and IEEE 118-bus systems reveal the over-conservatism of standard Wasserstein DRO under low-probability extreme events and validate the effectiveness and efficiency of the proposed planning model and solution algorithm.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Xinda Shen",
        "Yifan Su",
        "Bo Hu",
        "Yanbo Chen",
        "Changzheng Shao",
        "Tao Wu"
      ],
      "published_at": "2026-10-13T12:39:04Z",
      "updated_at": "2026-10-13T12:39:04Z",
      "abstract": "The increasing penetration of renewable energy makes power systems more vulnerable to low-probability extreme events, elevating system planning to a critical role in ensuring reliability. Distributionally robust optimization (DRO) based on the Wasserstein distance is widely used to address this challenge. However, we find that the conventional Wasserstein ambiguity set tends to excessively amplify the probabilities of low-probability extreme events, leading to over-conservative investment decisions. To mitigate this over-conservatism, this paper proposes a novel ambiguity set that modifies the Wasserstein distance with a Kullback-Leibler (KL) divergence regularization term. By leveraging the KL divergence's sensitivity to relative probability changes, the proposed set effectively curbs the unrealistic magnification of low-probability events. A two-stage distributionally robust planning model built upon this new ambiguity set is developed to optimize the installed capacity of photovoltaic, wind, and energy storage. The complex model is then reformulated as an equivalent exponential cone program with strict theoretical proofs, making it computationally tractable. Numerical results on the PJM 5-bus and IEEE 118-bus systems reveal the over-conservatism of standard Wasserstein DRO under low-probability extreme events and validate the effectiveness and efficiency of the proposed planning model and solution algorithm.",
      "source_url": "https://arxiv.org/abs/2610.18290",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "시뮬레이션",
        "데이터 분석"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-25T07:40:43Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "도로교통": [
            "road traffic"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulations"
          ],
          "데이터 분석": [
            "time series"
          ]
        }
      },
      "id": "auto-arxiv-2609-32381",
      "arxiv_id": "2609.32381",
      "title": "Portmanteau Goodness-of-Fit Tests in the Presence of Simple, Mixed, and Multiple Seasonal Autocorrelation",
      "summary_ko": "Standard diagnostic procedures for assessing the goodness of fit of linear models include tests of the null hypothesis of no residual autocorrelation against the alternative of linear dependence. The literature proposes several portmanteau tests for residual autocorrelation in SARMA models, mainly focusing on two cases: short-term and single-seasonal autocorrelation. Since many time series exhibit multiple seasonal patterns, whose periodic components may interact with one another, diagnostic tests for residual autocorrelation in a multi-seasonal framework are needed. However, the literature still lacks portmanteau tests specifically designed for multiple seasonal autocorrelation. This paper addresses this gap by extending classical portmanteau tests to settings with multiple seasonalities. In addition, the proposed approach jointly tests for both short-term and multiple seasonal residual autocorrelation. Test statistics and their asymptotic distributions are defined. Then, Monte Carlo simulations are used to evaluate the performance of the tests and the consistency with the expected results is assessed using statistical tests. The results suggest that the proposed extension can serve as a useful goodness-of-fit diagnostic tool for the class of mSARIMA models. An application to road traffic data is also provided.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Matteo Grigoletto",
        "Francesco Lisi"
      ],
      "published_at": "2026-09-26T08:55:52Z",
      "updated_at": "2026-10-05T13:42:48Z",
      "abstract": "Standard diagnostic procedures for assessing the goodness of fit of linear models include tests of the null hypothesis of no residual autocorrelation against the alternative of linear dependence. The literature proposes several portmanteau tests for residual autocorrelation in SARMA models, mainly focusing on two cases: short-term and single-seasonal autocorrelation. Since many time series exhibit multiple seasonal patterns, whose periodic components may interact with one another, diagnostic tests for residual autocorrelation in a multi-seasonal framework are needed. However, the literature still lacks portmanteau tests specifically designed for multiple seasonal autocorrelation. This paper addresses this gap by extending classical portmanteau tests to settings with multiple seasonalities. In addition, the proposed approach jointly tests for both short-term and multiple seasonal residual autocorrelation. Test statistics and their asymptotic distributions are defined. Then, Monte Carlo simulations are used to evaluate the performance of the tests and the consistency with the expected results is assessed using statistical tests. The results suggest that the proposed extension can serve as a useful goodness-of-fit diagnostic tool for the class of mSARIMA models. An application to road traffic data is also provided.",
      "source_url": "https://arxiv.org/abs/2609.32381",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇",
        "제조"
      ],
      "techniques": [
        "AI",
        "시뮬레이션",
        "디지털트윈"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-25T07:40:43Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robotics"
          ],
          "제조": [
            "manufacturing"
          ]
        },
        "technique_matches": {
          "AI": [
            "AI",
            "artificial intelligence"
          ],
          "시뮬레이션": [
            "simulation"
          ],
          "디지털트윈": [
            "digital twins"
          ]
        }
      },
      "id": "auto-arxiv-2610-27045",
      "arxiv_id": "2610.27045",
      "title": "Physical AI: Concepts, Techniques, and Challenges",
      "summary_ko": "In recent years, physical artificial intelligence (physical AI) has experienced rapid development, capturing significant attention in academia and industry. However, the field has yet to reach consensus on its definition, technical landscape, and core challenges. To address this gap, this monograph seeks to define physical AI, present its technical landscape, and examine each key technical subfield in depth. In doing so, it establishes a unified framework for Physical AI: artificial systems that perceive, model, and act upon the physical world through continuous feedback under real-world constraints. Further, this monograph distinguishes physical AI from conventional digital AI, embodied AI, cyber-physical systems, and digital twins. It develops the foundations of physical AI across physics, control, cognition, learning, and reasoning. It reviews key methodologies, including multimodal perception, physical data and simulation, world models, embodied foundation models, motor-control systems, multi-agent coordination, and hardware-software co-design. Furthermore, it examines applications spanning robotics, manufacturing, infrastructure, scientific experimentation, and autonomous services. Particular attention is given to sim-to-real transfer, reliability, real-time execution, evaluation, safety, governance, and long-term deployment. By connecting perception, prediction, decision-making, action, and adaptation in a closed loop, this monograph identifies the technical challenges and research directions required to advance from narrow automation toward more generalizable physical AI and, ultimately, physical artificial general intelligence (Physical AGI).",
      "first_seen_at": "2026-10-21T07:48:46Z",
      "discovery_sources": [],
      "authors": [
        "Weinan Zhang",
        "Chunhua Shen",
        "Tianfan Xue",
        "Bin Zhao",
        "Bingqi Jiang",
        "Zihan Guo",
        "Zicai Cui",
        "Yang Li",
        "Ming Zhou",
        "Xinyuan Chen",
        "Lihe Ding",
        "Xiao Chen",
        "Yujin Wang",
        "Shi Guo",
        "Junting Dong",
        "Tao Lu",
        "Xing Shen",
        "Haoyu Guo",
        "Yao Mu",
        "Haiyong Yu",
        "Haoxiang Ma",
        "Fuxian Huang",
        "Qiaojun Yu",
        "Yunsong Zhou",
        "Kailin Li",
        "Zirui Wang",
        "Yufei Xue",
        "Weishuai Zeng",
        "Tao Huang",
        "Zhigang Wang",
        "Junli Liu",
        "Pengyuan Wu",
        "Pingrui Zhang",
        "Haotian Wang",
        "Dongzhan Zhou",
        "Jiafei Cao",
        "Feng Yuan",
        "Xinyi Ye",
        "Quanli Xuan",
        "Mulin Yu",
        "Shangding Gu",
        "Kaiwen Song",
        "Dong Wang",
        "Jiarui Li",
        "Kehui Liu",
        "Zhongjie Jia",
        "Dongjiang Li",
        "Jiacheng Bao",
        "Haoran Yang",
        "Zhaoqing Chen",
        "Yiming Sun",
        "Bowen Zhou"
      ],
      "published_at": "2026-10-19T17:50:11Z",
      "updated_at": "2026-10-19T17:50:11Z",
      "abstract": "In recent years, physical artificial intelligence (physical AI) has experienced rapid development, capturing significant attention in academia and industry. However, the field has yet to reach consensus on its definition, technical landscape, and core challenges. To address this gap, this monograph seeks to define physical AI, present its technical landscape, and examine each key technical subfield in depth. In doing so, it establishes a unified framework for Physical AI: artificial systems that perceive, model, and act upon the physical world through continuous feedback under real-world constraints. Further, this monograph distinguishes physical AI from conventional digital AI, embodied AI, cyber-physical systems, and digital twins. It develops the foundations of physical AI across physics, control, cognition, learning, and reasoning. It reviews key methodologies, including multimodal perception, physical data and simulation, world models, embodied foundation models, motor-control systems, multi-agent coordination, and hardware-software co-design. Furthermore, it examines applications spanning robotics, manufacturing, infrastructure, scientific experimentation, and autonomous services. Particular attention is given to sim-to-real transfer, reliability, real-time execution, evaluation, safety, governance, and long-term deployment. By connecting perception, prediction, decision-making, action, and adaptation in a closed loop, this monograph identifies the technical challenges and research directions required to advance from narrow automation toward more generalizable physical AI and, ultimately, physical artificial general intelligence (Physical AGI).",
      "source_url": "https://arxiv.org/abs/2610.27045",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-25T07:40:43Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "Power Systems"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2610-18274",
      "arxiv_id": "2610.18274",
      "title": "Fast Analytical Method for Resource Adequacy Assessment in Renewable-Rich Power Systems",
      "summary_ko": "This paper proposes a novel analytical method for resource adequacy assessment in renewable rich power systems. The approach begins by applying the stress strength method to estimate two key resource adequacy indices: loss of load expectation (LOLE) and expected energy not supplied (EENS). This method requires defining the probability distribution functions (PDFs) for both the generation side and the load side. The load is assumed to follow a normal probability distribution, while the generation side probability distribution is derived from the combined PDFs of conventional, wind, and solar power sources. The approach models the cumulative impact of uncertainties on resource adequacy using the central limit theorem (CLT), and improves accuracy with a cumulant based Gram Charlier expansion when the CLT alone is insufficient. The effectiveness of the proposed method is validated through Monte Carlo simulations conducted on the IEEE single area and multi-area RTS-96 test systems. The results demonstrate that the proposed method achieves a substantial reduction in computational time, which is crucial in the context of operational planning, without sacrificing accuracy. Furthermore, its performance improves as renewable penetration increases, making it particularly suitable for future power systems operation and planning.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Seyed Amir Hosseini",
        "Mohammad Reza Karimi Gharigh",
        "Antonello Monti",
        "Peter Palensky",
        "Saeed Peyghami"
      ],
      "published_at": "2026-10-13T12:30:18Z",
      "updated_at": "2026-10-13T12:30:18Z",
      "abstract": "This paper proposes a novel analytical method for resource adequacy assessment in renewable rich power systems. The approach begins by applying the stress strength method to estimate two key resource adequacy indices: loss of load expectation (LOLE) and expected energy not supplied (EENS). This method requires defining the probability distribution functions (PDFs) for both the generation side and the load side. The load is assumed to follow a normal probability distribution, while the generation side probability distribution is derived from the combined PDFs of conventional, wind, and solar power sources. The approach models the cumulative impact of uncertainties on resource adequacy using the central limit theorem (CLT), and improves accuracy with a cumulant based Gram Charlier expansion when the CLT alone is insufficient. The effectiveness of the proposed method is validated through Monte Carlo simulations conducted on the IEEE single area and multi-area RTS-96 test systems. The results demonstrate that the proposed method achieves a substantial reduction in computational time, which is crucial in the context of operational planning, without sacrificing accuracy. Furthermore, its performance improves as renewable penetration increases, making it particularly suitable for future power systems operation and planning.",
      "source_url": "https://arxiv.org/abs/2610.18274",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "AI",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-26T01:32:49Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robot"
          ]
        },
        "technique_matches": {
          "AI": [
            "neural networks"
          ],
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-23010",
      "arxiv_id": "2610.23010",
      "title": "What Makes a Good Symbol for Hybrid Discrete-Continuous Planning?",
      "summary_ko": "The physical world has continuous state and action spaces, yet robot planning often benefits from hybrid discrete-continuous formulations that explicitly expose combinatorial structure, such as contact modes or discrete primitive actions. Existing methods typically rely on human-defined symbol sets or symbols derived from language supervision, without asking what makes a symbol computationally useful. In this paper, we take an inference-algorithm--centric view to define usefulness: a good symbol should expose the discrete commitments that simplify solving the original planning problem. We first introduce the concept of solver-sufficient symbolization by studying finite-horizon piecewise-affine systems, for which solver sufficiency can be precisely characterized, and develop a counterexample-guided algorithm for symbol invention. We then extend this principle to invent symbols for optimization objectives parameterized by general neural networks as energy functions, showing that solver-sufficient symbols can nevertheless be induced from their continuous optimization landscapes. Experiments on simulated and real-robot tasks show that the resulting symbols reduce solving cost while preserving solution quality, while also improving interpretability and steerability. Furthermore, our theoretical and algorithmic insights help explain why certain human-defined symbols are computationally useful.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Yiyuan Pan",
        "Changliu Liu",
        "Jiayuan Mao"
      ],
      "published_at": "2026-10-16T06:42:46Z",
      "updated_at": "2026-10-16T06:42:46Z",
      "abstract": "The physical world has continuous state and action spaces, yet robot planning often benefits from hybrid discrete-continuous formulations that explicitly expose combinatorial structure, such as contact modes or discrete primitive actions. Existing methods typically rely on human-defined symbol sets or symbols derived from language supervision, without asking what makes a symbol computationally useful. In this paper, we take an inference-algorithm--centric view to define usefulness: a good symbol should expose the discrete commitments that simplify solving the original planning problem. We first introduce the concept of solver-sufficient symbolization by studying finite-horizon piecewise-affine systems, for which solver sufficiency can be precisely characterized, and develop a counterexample-guided algorithm for symbol invention. We then extend this principle to invent symbols for optimization objectives parameterized by general neural networks as energy functions, showing that solver-sufficient symbols can nevertheless be induced from their continuous optimization landscapes. Experiments on simulated and real-robot tasks show that the resulting symbols reduce solving cost while preserving solution quality, while also improving interpretability and steerability. Furthermore, our theoretical and algorithmic insights help explain why certain human-defined symbols are computationally useful.",
      "source_url": "https://arxiv.org/abs/2610.23010",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "시뮬레이션",
        "데이터 분석"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-26T01:32:49Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power system"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "Simulations"
          ],
          "데이터 분석": [
            "State estimation"
          ]
        }
      },
      "id": "auto-arxiv-2610-16556",
      "arxiv_id": "2610.16556",
      "title": "Design of Privacy-Preserving Estimators Under Prior Information Exposure",
      "summary_ko": "State estimation is widely used in networked dynamical systems, but released estimates may inadvertently reveal sensitive information about private state variables. Existing privacy-preserving estimation frameworks often do not explicitly account for adversarial prior knowledge. Motivated by this gap, we study privacy-preserving state estimation for discrete-time linear stochastic systems under prior information exposure using pointwise maximal leakage (PML). We first reveal a privacy anti-optimality property of the Kalman filter: in the absence of additive release noise, the steady-state Kalman filter maximizes PML privacy leakage among the considered class of stable linear estimators. We further establish a nontrivial lower bound on the steady-state estimation error enforced by $(\\varepsilon,δ)$-PML privacy. We then derive sufficient release-noise conditions that guarantee $(\\varepsilon,δ)$-PML privacy, first for the Kalman filter and then for stable linear estimators. Finally, a convex semidefinite program is formulated for a joint design of the estimator gain and release noise covariance, providing a tractable privacy-preserving estimator with an explicit upper bound on the steady-state estimation error. Simulations on a source--load power system demonstrate the proposed privacy--utility tradeoff.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Le Liu",
        "Yu Kawano",
        "Ming Cao"
      ],
      "published_at": "2026-10-12T10:35:40Z",
      "updated_at": "2026-10-12T10:35:40Z",
      "abstract": "State estimation is widely used in networked dynamical systems, but released estimates may inadvertently reveal sensitive information about private state variables. Existing privacy-preserving estimation frameworks often do not explicitly account for adversarial prior knowledge. Motivated by this gap, we study privacy-preserving state estimation for discrete-time linear stochastic systems under prior information exposure using pointwise maximal leakage (PML). We first reveal a privacy anti-optimality property of the Kalman filter: in the absence of additive release noise, the steady-state Kalman filter maximizes PML privacy leakage among the considered class of stable linear estimators. We further establish a nontrivial lower bound on the steady-state estimation error enforced by $(\\varepsilon,δ)$-PML privacy. We then derive sufficient release-noise conditions that guarantee $(\\varepsilon,δ)$-PML privacy, first for the Kalman filter and then for stable linear estimators. Finally, a convex semidefinite program is formulated for a joint design of the estimator gain and release noise covariance, providing a tractable privacy-preserving estimator with an explicit upper bound on the steady-state estimation error. Simulations on a source--load power system demonstrate the proposed privacy--utility tradeoff.",
      "source_url": "https://arxiv.org/abs/2610.16556",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "AI",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-26T01:32:49Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "도로교통": [
            "traffic signal"
          ]
        },
        "technique_matches": {
          "AI": [
            "Reinforcement Learning"
          ],
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2609-30578",
      "arxiv_id": "2609.30578",
      "title": "Reinforcement Learning of Communication in a Mesh of Small Language Models",
      "summary_ko": "Language models gain accuracy from more compute at test time, but majority voting over independent samples saturates: as samples grow, the vote converges to the model's most frequent answer. Communication can add what sampling cannot: an agent that solves a problem can pass the key step to the others. We present TalkMesh, a decentralized mesh of small language model agents that learns when and what to communicate. Each agent samples a proposal and scores it with a trained confidence head. The most confident agent broadcasts a hint; agents below a confidence threshold revise, keeping each revision that outscores its proposal. Gossip consensus approximates the vote weighted by confidence without a coordinator. A talk policy, trained with group relative policy optimization on the change in correctness after revision, writes hints and revisions. With three agents, which together generate at most six outputs, the mesh reaches the accuracy of majority voting over 32 samples with each of three models. Trained with at most 8 agents and evaluated with 32, it raises accuracy from 0.568 under self-consistency to 0.705 (Qwen3.5-0.8B, GSM8K) and from 0.492 to 0.722 (SmolLM3-3B, MATH-500). When 4 of 8 agents collude on a wrong answer with fabricated confidence and poisoned hints, majority vote accuracy falls to 0.000 (Qwen3.5-0.8B, GSM8K). A defended mesh, whose agents rescore solutions with their own confidence heads, retains 0.507. Across reasoning, embodied coordination, and traffic signal control, messages improve a decision when the acting agent cannot observe the information it requires and another agent can send it.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Mehmet Kerem Turkcan"
      ],
      "published_at": "2026-09-24T21:41:12Z",
      "updated_at": "2026-09-24T21:41:12Z",
      "abstract": "Language models gain accuracy from more compute at test time, but majority voting over independent samples saturates: as samples grow, the vote converges to the model's most frequent answer. Communication can add what sampling cannot: an agent that solves a problem can pass the key step to the others. We present TalkMesh, a decentralized mesh of small language model agents that learns when and what to communicate. Each agent samples a proposal and scores it with a trained confidence head. The most confident agent broadcasts a hint; agents below a confidence threshold revise, keeping each revision that outscores its proposal. Gossip consensus approximates the vote weighted by confidence without a coordinator. A talk policy, trained with group relative policy optimization on the change in correctness after revision, writes hints and revisions. With three agents, which together generate at most six outputs, the mesh reaches the accuracy of majority voting over 32 samples with each of three models. Trained with at most 8 agents and evaluated with 32, it raises accuracy from 0.568 under self-consistency to 0.705 (Qwen3.5-0.8B, GSM8K) and from 0.492 to 0.722 (SmolLM3-3B, MATH-500). When 4 of 8 agents collude on a wrong answer with fabricated confidence and poisoned hints, majority vote accuracy falls to 0.000 (Qwen3.5-0.8B, GSM8K). A defended mesh, whose agents rescore solutions with their own confidence heads, retains 0.507. Across reasoning, embodied coordination, and traffic signal control, messages improve a decision when the acting agent cannot observe the information it requires and another agent can send it.",
      "source_url": "https://arxiv.org/abs/2609.30578",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지",
        "철도"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-26T01:32:49Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power system",
            "power flow"
          ],
          "철도": [
            "railway"
          ]
        },
        "technique_matches": {
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2609-16857",
      "arxiv_id": "2609.16857",
      "title": "Two-stage Coordinated Energy Management of Train Operation and Wayside Energy Storage System for Rail Power Supply Systems",
      "summary_ko": "The increasing electrification of railway power supply system (RPSS) intensify the operational and economic challenges at the railway power system interface. Energy storage systems (ESSs) can provide fast and flexible support to mitigate short term power spikes and to improve the energy management. However, achieving coordinated operation is challenged by the tight coupling among electrical railway operation and ESS dispatch under time-varying traction demand and network limits. This paper proposes a two-stage coordinated energy management method for electrified RPSSs that jointly optimizes railway system operation, train trajectories and ESS dispatch while explicitly accounting for traction power flow constraints. First, a day-ahead operation stage determines the train operating profiles and the ESS setting decisions to establish the baseline operating plan. Then, an intra-day rolling optimization stage based on adaptive weight economic-model predictive control (AWC-MPC) updates ESS dispatch under refreshed forecasts of traction demand and renewable output. A real Swedish railway case is utilized to minimize the energy purchase cost and the ESS cost while reducing peak grid power, demonstrating its practical applicability with 35.7% peak grid power demand and 28.8% total system cost reduction.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Fei Liu",
        "Niklas Biedermann",
        "Stefan Östlund",
        "Qianwen Xu"
      ],
      "published_at": "2026-09-15T08:49:05Z",
      "updated_at": "2026-09-15T08:49:05Z",
      "abstract": "The increasing electrification of railway power supply system (RPSS) intensify the operational and economic challenges at the railway power system interface. Energy storage systems (ESSs) can provide fast and flexible support to mitigate short term power spikes and to improve the energy management. However, achieving coordinated operation is challenged by the tight coupling among electrical railway operation and ESS dispatch under time-varying traction demand and network limits. This paper proposes a two-stage coordinated energy management method for electrified RPSSs that jointly optimizes railway system operation, train trajectories and ESS dispatch while explicitly accounting for traction power flow constraints. First, a day-ahead operation stage determines the train operating profiles and the ESS setting decisions to establish the baseline operating plan. Then, an intra-day rolling optimization stage based on adaptive weight economic-model predictive control (AWC-MPC) updates ESS dispatch under refreshed forecasts of traction demand and renewable output. A real Swedish railway case is utilized to minimize the energy purchase cost and the ESS cost while reducing peak grid power, demonstrating its practical applicability with 35.7% peak grid power demand and 28.8% total system cost reduction.",
      "source_url": "https://arxiv.org/abs/2609.16857",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "우주"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-26T01:32:49Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "우주": [
            "satellites"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2610-30922",
      "arxiv_id": "2610.30922",
      "title": "Stability of rotation axis of galactic disks in HESTIA suite",
      "summary_ko": "We have found that in approximately 1/4 of Milky Way or M31 analogues in the HESTIA suite of simulations there is a significant, $>45^\\circ$, change in the axis of rotation orientation of the gaseous disk during the last 6 Gyr. In 6 out of 7 such cases the change is likely caused by the accretion of individual satellites, while in one case such a connection is not obvious. All galaxies that underwent a significant change in rotation axis direction also had angular momentum amplitudes below the sample median 6 Gyr ago. Our findings support the idea that last 6~Gyr was a quiet phase of evolution in the LG. On the other hand, the mechanism we detect, that satellites can flip galaxy orientation, can explain the difficulties in identifying correlations between the rotation axis and the orientation of large-scale structure elements.",
      "first_seen_at": "2026-10-23T07:42:21Z",
      "discovery_sources": [],
      "authors": [
        "Naira R. Arakelyan",
        "Sergey V. Pilipenko",
        "Stefan Gottlöber",
        "Noam I. Libeskind",
        "Gustavo Yepes",
        "Yehuda Hoffman"
      ],
      "published_at": "2026-10-22T08:42:13Z",
      "updated_at": "2026-10-22T08:42:13Z",
      "abstract": "We have found that in approximately 1/4 of Milky Way or M31 analogues in the HESTIA suite of simulations there is a significant, $>45^\\circ$, change in the axis of rotation orientation of the gaseous disk during the last 6 Gyr. In 6 out of 7 such cases the change is likely caused by the accretion of individual satellites, while in one case such a connection is not obvious. All galaxies that underwent a significant change in rotation axis direction also had angular momentum amplitudes below the sample median 6 Gyr ago. Our findings support the idea that last 6~Gyr was a quiet phase of evolution in the LG. On the other hand, the mechanism we detect, that satellites can flip galaxy orientation, can explain the difficulties in identifying correlations between the rotation axis and the orientation of large-scale structure elements.",
      "source_url": "https://arxiv.org/abs/2610.30922",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "제조"
      ],
      "techniques": [
        "시뮬레이션",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-26T01:32:49Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "제조": [
            "manufacturing",
            "production-line"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulator"
          ],
          "최적화": [
            "optimisation"
          ]
        }
      },
      "id": "auto-arxiv-2610-31147",
      "arxiv_id": "2610.31147",
      "title": "PriMa-Causa: Causal Prescriptive Maintenance",
      "summary_ko": "Automated manufacturing requires data-driven decisions under uncertainty and sparse observational records, and unplanned downtime is a dominant cost of getting them wrong. Prescriptive maintenance recommends a maintenance action against an impending failure rather than merely predicting it. Established reference architectures couple predictive models with rule- or optimisation-based recommendation logic. Selecting a corrective action, however, requires an interventional quantity: how the line behaviour would change if the action were set. Manufacturing data is observational, so a model fitted to predict the outcome estimates an associational quantity and may misdirect the choice of action without the prediction error revealing it. Causal estimators target the interventional quantity but require an expert-led causal study per case, for which the bounded decision window of operation leaves no room. This dissertation analyses how causal reasoning can be embedded into prescriptive maintenance so that actions are ranked by their estimated interventional effect. The result is PriMa-Causa, built from three components: a causal reasoning layer that integrates the estimate into the PriMa reference model, a parametric simulator that supplies the interventional ground truth absent from manufacturing data, and a graph-conditioned tabular causal foundation model that, pre-trained once on this synthetic production-line prior, answers interventional queries in a single forward pass. PriMa-Causa attains a lower prescription error than associational baselines, with the largest margin under weak overlap and sparse context, and a partially disclosed causal graph reduces this error further. The causal inductive bias of the synthetic prior and the graph conditioning can thus replace per-case estimator fitting, a step toward automating causal inference for autonomous and self-healing maintenance.",
      "first_seen_at": "2026-10-23T07:42:21Z",
      "discovery_sources": [],
      "authors": [
        "Felix Saretzky"
      ],
      "published_at": "2026-10-22T12:06:59Z",
      "updated_at": "2026-10-22T12:06:59Z",
      "abstract": "Automated manufacturing requires data-driven decisions under uncertainty and sparse observational records, and unplanned downtime is a dominant cost of getting them wrong. Prescriptive maintenance recommends a maintenance action against an impending failure rather than merely predicting it. Established reference architectures couple predictive models with rule- or optimisation-based recommendation logic. Selecting a corrective action, however, requires an interventional quantity: how the line behaviour would change if the action were set. Manufacturing data is observational, so a model fitted to predict the outcome estimates an associational quantity and may misdirect the choice of action without the prediction error revealing it. Causal estimators target the interventional quantity but require an expert-led causal study per case, for which the bounded decision window of operation leaves no room. This dissertation analyses how causal reasoning can be embedded into prescriptive maintenance so that actions are ranked by their estimated interventional effect. The result is PriMa-Causa, built from three components: a causal reasoning layer that integrates the estimate into the PriMa reference model, a parametric simulator that supplies the interventional ground truth absent from manufacturing data, and a graph-conditioned tabular causal foundation model that, pre-trained once on this synthetic production-line prior, answers interventional queries in a single forward pass. PriMa-Causa attains a lower prescription error than associational baselines, with the largest margin under weak overlap and sparse context, and a partially disclosed causal graph reduces this error further. The causal inductive bias of the synthetic prior and the graph conditioning can thus replace per-case estimator fitting, a step toward automating causal inference for autonomous and self-healing maintenance.",
      "source_url": "https://arxiv.org/abs/2610.31147",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-27T02:19:57Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power-system"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2610-16075",
      "arxiv_id": "2610.16075",
      "title": "Adaptive Calibration of Day-Ahead Flexibility Reserve Requirements Under Distribution Shift",
      "summary_ko": "The increasing integration of variable renewable generation introduces weather-driven variability and uncertainty into power-system operations. System operators manage the resulting deviations between day-ahead forecasts and real-time net load by scheduling upward and downward flexibility reserves. These requirements have traditionally been set using simple deterministic rules. Newer data-driven methods tailor reserves to forecasts and operating conditions. However, their reliability may deteriorate when conditions depart from the historical data. We address this limitation with a sequential framework that updates day-ahead reserve requirements as operating outcomes become available. Within this framework, Adaptive Conformal Inference targets the frequency of insufficient reserve, whereas Rolling Risk Control targets expected uncovered deviation, the mean residual beyond the scheduled reserve. We prove finite-horizon bounds on realized miscoverage and capped uncovered-deviation loss. We test the framework on real ERCOT data in a chronological deployment simulation that respects operational timing: outcomes from one day first affect the reserve schedule two days later. Training and validation use 2022--2023 data; the uninterrupted test covers 2024--2025. Coverage calibration achieves approximately 90% coverage, with upward and downward miscoverage near 5%. Risk calibration brings expected uncovered deviations close to their budgets while using 5.3% less reserve than the uncalibrated requirements. The framework thus produces adaptive reserve requirements that provably control the selected reliability metrics over finite horizons without assuming stationarity or exchangeability.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Napoleon Costilla-Enriquez",
        "Yang Weng",
        "Miguel A. Ortega-Vazquez"
      ],
      "published_at": "2026-10-12T02:31:33Z",
      "updated_at": "2026-10-12T02:31:33Z",
      "abstract": "The increasing integration of variable renewable generation introduces weather-driven variability and uncertainty into power-system operations. System operators manage the resulting deviations between day-ahead forecasts and real-time net load by scheduling upward and downward flexibility reserves. These requirements have traditionally been set using simple deterministic rules. Newer data-driven methods tailor reserves to forecasts and operating conditions. However, their reliability may deteriorate when conditions depart from the historical data. We address this limitation with a sequential framework that updates day-ahead reserve requirements as operating outcomes become available. Within this framework, Adaptive Conformal Inference targets the frequency of insufficient reserve, whereas Rolling Risk Control targets expected uncovered deviation, the mean residual beyond the scheduled reserve. We prove finite-horizon bounds on realized miscoverage and capped uncovered-deviation loss. We test the framework on real ERCOT data in a chronological deployment simulation that respects operational timing: outcomes from one day first affect the reserve schedule two days later. Training and validation use 2022--2023 data; the uninterrupted test covers 2024--2025. Coverage calibration achieves approximately 90% coverage, with upward and downward miscoverage near 5%. Risk calibration brings expected uncovered deviations close to their budgets while using 5.3% less reserve than the uncalibrated requirements. The framework thus produces adaptive reserve requirements that provably control the selected reliability metrics over finite horizons without assuming stationarity or exchangeability.",
      "source_url": "https://arxiv.org/abs/2610.16075",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "AI",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-27T02:19:57Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "도로교통": [
            "Traffic Signal",
            "traffic-signal"
          ]
        },
        "technique_matches": {
          "AI": [
            "Reinforcement Learning"
          ],
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2609-27528",
      "arxiv_id": "2609.27528",
      "title": "MDRC: A Deployable State-Recovery Defense for Traffic Signal Control under Sensor Corruption",
      "summary_ko": "Traffic Signal Control (TSC) is a safety-critical cyber-physical system that relies on real-time sensing. Corrupted observations caused by adversarial perturbations or sensor failures can propagate from the sensing layer into the controller and degrade traffic efficiency. Existing robust Reinforcement Learning (RL)-based TSC methods often suffer from limited cross-city generalization, high inference latency, and weak recovery under partial observability. We present MDRC (Meta-Diffusion-based framework for Resilient traffic signal Control against adversarial attacks and sensor failures), a post-detection state-recovery defense inserted between sensing and control. MDRC reconstructs trustworthy traffic states before they are consumed by the controller. It combines Denoising Diffusion Implicit Models (DDIM) for efficient state recovery with Reptile meta-learning for a transferable initialization across cities. We provide an optimization-based view of the DDIM recovery dynamics and establish a recovery-error bound that separates score approximation, numerical discretization, and initialization mismatch. Across seven real-world-derived CityFlow benchmarks, MDRC reduces Average Travel Time by 6.77% under stochastic and policy-aware attacks and by 12.75% under structured sensor loss, while improving state-recovery fidelity. We further evaluate 3,600 seconds of real roadside measurements with 50% of detector channels disabled and integrate MDRC into a hardware-in-the-loop traffic-signal stack. Over a 9.16-hour run with 32,389 sensing/control cycles, the system achieves 99.79% decision availability, produces no out-of-plan recommendations, and requires approximately 38 ms of component-wise processing per one-second control interval.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Mingyuan Li",
        "Chunyu Liu",
        "Xiao Liu",
        "Yanna Jiang",
        "Guangsheng Yu",
        "Xu Wang",
        "Wei Ni",
        "Ren Ping Liu"
      ],
      "published_at": "2026-09-23T08:22:11Z",
      "updated_at": "2026-09-23T08:22:11Z",
      "abstract": "Traffic Signal Control (TSC) is a safety-critical cyber-physical system that relies on real-time sensing. Corrupted observations caused by adversarial perturbations or sensor failures can propagate from the sensing layer into the controller and degrade traffic efficiency. Existing robust Reinforcement Learning (RL)-based TSC methods often suffer from limited cross-city generalization, high inference latency, and weak recovery under partial observability. We present MDRC (Meta-Diffusion-based framework for Resilient traffic signal Control against adversarial attacks and sensor failures), a post-detection state-recovery defense inserted between sensing and control. MDRC reconstructs trustworthy traffic states before they are consumed by the controller. It combines Denoising Diffusion Implicit Models (DDIM) for efficient state recovery with Reptile meta-learning for a transferable initialization across cities. We provide an optimization-based view of the DDIM recovery dynamics and establish a recovery-error bound that separates score approximation, numerical discretization, and initialization mismatch. Across seven real-world-derived CityFlow benchmarks, MDRC reduces Average Travel Time by 6.77% under stochastic and policy-aware attacks and by 12.75% under structured sensor loss, while improving state-recovery fidelity. We further evaluate 3,600 seconds of real roadside measurements with 50% of detector channels disabled and integrate MDRC into a hardware-in-the-loop traffic-signal stack. Over a 9.16-hour run with 32,389 sensing/control cycles, the system achieves 99.79% decision availability, produces no out-of-plan recommendations, and requires approximately 38 ms of component-wise processing per one-second control interval.",
      "source_url": "https://arxiv.org/abs/2609.27528",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "철도"
      ],
      "techniques": [
        "AI",
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-27T02:19:57Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "철도": [
            "Railway"
          ]
        },
        "technique_matches": {
          "AI": [
            "neural network"
          ],
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2609-12018",
      "arxiv_id": "2609.12018",
      "title": "Toward Reliable Railway-Bogie Response Prediction Using Multifidelity TDNN and Physics-Informed Residual Learning",
      "summary_ko": "Railway engineers need simulation models that predict vehicle responses across operating scenarios that cannot be tested exhaustively. Agreement with representative measurements provides essential evidence, but calibration at a limited set of conditions does not guarantee accuracy elsewhere. We present a multifidelity railway-bogie response-correction method that treats multibody simulation histories as low-fidelity information and roller-rig measurements as high-fidelity evidence. This method combines an experiment-anchored fidelity assignment with physics-informed discrepancy learning for multichannel bogie-response histories. A time-delay neural network (TDNN) represents the condition-dependent simulation trend, and development-fitted amplitude alignment defines the low-fidelity baseline. A residual-correction network then models the reproducible response component not explained by this baseline and adds it to the baseline. An effective dynamic-balance equation constrains the learned discrepancy by representing differences in inertia, damping, stiffness, and external forcing between the simulated and physical systems. The training objective combines this constraint with residual matching, temporal smoothness, and a combined channel-2 acceleration loss selected using displacement-acceleration consistency evidence. For the evaluated reconstruction case, the corrected response gives a mean coefficient of determination of 0.8197, a mean normalized root-mean-square error (NRMSE) of 4.6055 %, and a mean normalized mean absolute error (NMAE) of 1.9297 %. These results provide initial evidence of accurate response prediction at the held-out 385 km/h condition.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Gyeolhee Lee",
        "Moosun Kim",
        "Taewook Kwon",
        "Jaehun Kim",
        "Changsung Jeon",
        "Dongjin Lee"
      ],
      "published_at": "2026-09-10T07:56:53Z",
      "updated_at": "2026-09-14T11:35:22Z",
      "abstract": "Railway engineers need simulation models that predict vehicle responses across operating scenarios that cannot be tested exhaustively. Agreement with representative measurements provides essential evidence, but calibration at a limited set of conditions does not guarantee accuracy elsewhere. We present a multifidelity railway-bogie response-correction method that treats multibody simulation histories as low-fidelity information and roller-rig measurements as high-fidelity evidence. This method combines an experiment-anchored fidelity assignment with physics-informed discrepancy learning for multichannel bogie-response histories. A time-delay neural network (TDNN) represents the condition-dependent simulation trend, and development-fitted amplitude alignment defines the low-fidelity baseline. A residual-correction network then models the reproducible response component not explained by this baseline and adds it to the baseline. An effective dynamic-balance equation constrains the learned discrepancy by representing differences in inertia, damping, stiffness, and external forcing between the simulated and physical systems. The training objective combines this constraint with residual matching, temporal smoothness, and a combined channel-2 acceleration loss selected using displacement-acceleration consistency evidence. For the evaluated reconstruction case, the corrected response gives a mean coefficient of determination of 0.8197, a mean normalized root-mean-square error (NRMSE) of 4.6055 %, and a mean normalized mean absolute error (NMAE) of 1.9297 %. These results provide initial evidence of accurate response prediction at the held-out 385 km/h condition.",
      "source_url": "https://arxiv.org/abs/2609.12018",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "데이터 분석"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-27T02:19:57Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robotic"
          ]
        },
        "technique_matches": {
          "데이터 분석": [
            "point cloud"
          ]
        }
      },
      "id": "auto-arxiv-2610-29647",
      "arxiv_id": "2610.29647",
      "title": "Reading the Room: Scale-ambiguous Semantic Registration of Sparse VSLAM Maps to Floor Plans in Partially Built Environments",
      "summary_ko": "Many robotic construction applications, such as progress tracking, require registering a sensor trajectory to a prior 2D floor plan. Visual capture is attractive for its low cost and availability, but aligning a visual simultaneous localization and mapping (VSLAM) map to a floor plan is difficult; VSLAM recovers geometry only up to an unknown similarity transform, the sparse point cloud is cross-modal to the symbolic floor plan, sites are only partially built during capture, and construction sites are visually repetitive, degrading map quality while removing distinctive features. Existing methods address at most two of these challenges. We thus introduce Reading the Room(RR), a global-local approach to register scale-ambiguous, sparse, semantic VSLAM maps to partially built floor plans. Our key idea is to define a single robust, differentiable, class-balanced semantic-geometric energy function that governs all stages of the pipeline. We develop two global initialization schemes, leveraging a single inaccurate user-provided correspondence if provided, or estimating the initialization globally using a cross-modal matching network. Both schemes are designed to find initial candidates with respect to the energy function, which is then directly optimized for local refinement. We evaluate RR on 33 sequences across three active construction sites against classical and learning-based baselines, achieving a 4.5x improvement in mean absolute trajectory error over the strongest baseline in the general unconstrained setting, which increases to 7.6x when an anchor is provided.",
      "first_seen_at": "2026-10-22T07:44:50Z",
      "discovery_sources": [],
      "authors": [
        "Bjorn Stragier",
        "Emilia Szymańska",
        "J. Micah Prendergast",
        "Lukas Rosenberger Schmid"
      ],
      "published_at": "2026-10-21T12:28:06Z",
      "updated_at": "2026-10-21T12:28:06Z",
      "abstract": "Many robotic construction applications, such as progress tracking, require registering a sensor trajectory to a prior 2D floor plan. Visual capture is attractive for its low cost and availability, but aligning a visual simultaneous localization and mapping (VSLAM) map to a floor plan is difficult; VSLAM recovers geometry only up to an unknown similarity transform, the sparse point cloud is cross-modal to the symbolic floor plan, sites are only partially built during capture, and construction sites are visually repetitive, degrading map quality while removing distinctive features. Existing methods address at most two of these challenges. We thus introduce Reading the Room(RR), a global-local approach to register scale-ambiguous, sparse, semantic VSLAM maps to partially built floor plans. Our key idea is to define a single robust, differentiable, class-balanced semantic-geometric energy function that governs all stages of the pipeline. We develop two global initialization schemes, leveraging a single inaccurate user-provided correspondence if provided, or estimating the initialization globally using a cross-modal matching network. Both schemes are designed to find initial candidates with respect to the energy function, which is then directly optimized for local refinement. We evaluate RR on 33 sequences across three active construction sites against classical and learning-based baselines, achieving a 4.5x improvement in mean absolute trajectory error over the strongest baseline in the general unconstrained setting, which increases to 7.6x when an anchor is provided.",
      "source_url": "https://arxiv.org/abs/2610.29647",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "제조"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-27T02:19:57Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "제조": [
            "manufacturing"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2610-31038",
      "arxiv_id": "2610.31038",
      "title": "Multichoice Modeling: Exploring Decision Configuration Space under Uncertainty",
      "summary_ko": "Decision support has become sophisticated at exploring uncertainty, but much less so at representing the architecture of the decision itself. As a result, the decision space is often narrowed before uncertainty is analyzed, leaving interactions among choices and the effects of alternatives they imply largely invisible. This paper introduces multichoice modeling, a framework for making large decision configuration spaces explicit and computationally explorable under uncertainty. Drawing on the logic of rugged landscapes, we conceptualize such problems as contingent performance landscapes, where interdependent choices generate configuration-specific outcomes whose value changes across uncertain futures. The computational framework combines multidimensional decision and uncertainty spaces in a single simulation. Further, variance-based sensitivity analysis is used for identifying influential choices, and conditional distribution analysis for comparing configuration-specific performance profiles. We demonstrate the framework through three case studies: a manufacturing process upgrade, M&A strategy, and mortgage design. By contrasting conventional uncertainty-only analysis with multichoice modeling, the cases show how opening the decision configuration space can reveal unexpected complementarities, near-best configurations with different risk profiles, and strategies that were not apparent as alternatives at the outset.",
      "first_seen_at": "2026-10-23T07:42:21Z",
      "discovery_sources": [],
      "authors": [
        "Mariia Kozlova",
        "Roman Stepanov",
        "Alexander Walzer",
        "Andrea Saltelli"
      ],
      "published_at": "2026-10-22T10:16:34Z",
      "updated_at": "2026-10-22T10:16:34Z",
      "abstract": "Decision support has become sophisticated at exploring uncertainty, but much less so at representing the architecture of the decision itself. As a result, the decision space is often narrowed before uncertainty is analyzed, leaving interactions among choices and the effects of alternatives they imply largely invisible. This paper introduces multichoice modeling, a framework for making large decision configuration spaces explicit and computationally explorable under uncertainty. Drawing on the logic of rugged landscapes, we conceptualize such problems as contingent performance landscapes, where interdependent choices generate configuration-specific outcomes whose value changes across uncertain futures. The computational framework combines multidimensional decision and uncertainty spaces in a single simulation. Further, variance-based sensitivity analysis is used for identifying influential choices, and conditional distribution analysis for comparing configuration-specific performance profiles. We demonstrate the framework through three case studies: a manufacturing process upgrade, M&A strategy, and mortgage design. By contrasting conventional uncertainty-only analysis with multichoice modeling, the cases show how opening the decision configuration space can reveal unexpected complementarities, near-best configurations with different risk profiles, and strategies that were not apparent as alternatives at the outset.",
      "source_url": "https://arxiv.org/abs/2610.31038",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "우주"
      ],
      "techniques": [
        "시뮬레이션",
        "디지털트윈",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-27T02:19:57Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "우주": [
            "satellite",
            "satellites"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulations"
          ],
          "디지털트윈": [
            "Digital Twin"
          ],
          "최적화": [
            "Optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-32827",
      "arxiv_id": "2610.32827",
      "title": "Multi-Timescale Context-Aware Radio Resource Optimization for Harmonious NTN-TN Coexistence: A Digital Twin and Open RAN Enabled Framework",
      "summary_ko": "Coexistence between non-terrestrial networks (NTNs) and terrestrial networks (TNs) is essential for future wireless systems, but terrestrial downlink transmissions can generate harmful aggregate interference at low-Earth orbit (LEO) satellite receivers. We present DT-STABLE, an Open Radio Access Network (O-RAN)-enabled, digital twin (DT)-assisted framework for context-aware NTN-TN coexistence. Using site-specific information, the DT estimates interference at LEO satellites, enabling proactive management with reduced real-time measurement overhead. We formulate joint beam activation and user association under RF-chain limits, load balancing, and aggregate satellite interference constraints; prove NP-hardness; and develop an efficient DT-guided scheduling algorithm. DT-STABLE couples short-timescale radio resource adaptation with periodic mid- and long-timescale DT updates through standardized O-RAN interfaces. Site-specific 28 GHz simulations under worst-case satellite geometry and low elevation angles show that DT-STABLE balances rate and interference utility, achieving 89.63% interference-to-noise ratio (INR) satisfaction and a mean sum rate of 210.96 bps/Hz, with nearly 100% INR satisfaction when satellite protection is prioritized. The framework reduces beam measurement overhead by 99.71% relative to exhaustive sweeping and achieves up to 33.1 times lower runtime than the Gurobi-based exact optimization benchmark.",
      "first_seen_at": "2026-10-26T07:55:22Z",
      "discovery_sources": [],
      "authors": [
        "Ecem Izde Kaya",
        "Md. Zoheb Hassan",
        "Georges Kaddoum"
      ],
      "published_at": "2026-10-23T16:01:07Z",
      "updated_at": "2026-10-23T16:01:07Z",
      "abstract": "Coexistence between non-terrestrial networks (NTNs) and terrestrial networks (TNs) is essential for future wireless systems, but terrestrial downlink transmissions can generate harmful aggregate interference at low-Earth orbit (LEO) satellite receivers. We present DT-STABLE, an Open Radio Access Network (O-RAN)-enabled, digital twin (DT)-assisted framework for context-aware NTN-TN coexistence. Using site-specific information, the DT estimates interference at LEO satellites, enabling proactive management with reduced real-time measurement overhead. We formulate joint beam activation and user association under RF-chain limits, load balancing, and aggregate satellite interference constraints; prove NP-hardness; and develop an efficient DT-guided scheduling algorithm. DT-STABLE couples short-timescale radio resource adaptation with periodic mid- and long-timescale DT updates through standardized O-RAN interfaces. Site-specific 28 GHz simulations under worst-case satellite geometry and low elevation angles show that DT-STABLE balances rate and interference utility, achieving 89.63% interference-to-noise ratio (INR) satisfaction and a mean sum rate of 210.96 bps/Hz, with nearly 100% INR satisfaction when satellite protection is prioritized. The framework reduces beam measurement overhead by 99.71% relative to exhaustive sweeping and achieves up to 33.1 times lower runtime than the Gurobi-based exact optimization benchmark.",
      "source_url": "https://arxiv.org/abs/2610.32827",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "AI",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-29T08:39:16Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "도로교통": [
            "Traffic Signal"
          ]
        },
        "technique_matches": {
          "AI": [
            "reinforcement learning"
          ],
          "최적화": [
            "Optimization"
          ]
        }
      },
      "id": "auto-arxiv-2609-22746",
      "arxiv_id": "2609.22746",
      "title": "ProcessLight: Process Supervision for Large Language Model Based Traffic Signal Control",
      "summary_ko": "Large Language Models (LLMs) have recently been introduced into traffic signal control (TSC) as decision agents due to their strengths in human-readable reasoning generation. Yet, existing LLM TSC methods optimize only from final outcomes and fail to distinguish valid from flawed reasoning steps, causing useful or misleading steps to be jointly updated and thus impairing the model's learning of effective reasoning. To bridge this gap, we propose an LLM-based framework ProcessLight to decompose signal decisions into verifiable semantic steps. Building on ProcessLight, we further develop Step-wise Traffic Process Policy Optimization (STeP-PO), a novel reinforcement learning framework that optimizes structured reasoning processes through step-level credit assignment. Specifically, STeP-PO uses step quality scores to evaluate local reasoning quality and step importance to measure each step's influence on the final action, and then assigns step-level advantages over a semantic step tree structure. The resulting step-level advantages are propagated to reasoning tokens, enabling fine-grained policy optimization beyond outcome-only rewards. Extensive experiments over multiple real-world datasets demonstrate the superiority of our methods. Our code is available at https://github.com/wenzhaoabc/processlight.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Huaitao Zhao",
        "Tianlong Zhou",
        "Weijie Wang",
        "Jiasheng Shi",
        "Weixiong Rao"
      ],
      "published_at": "2026-09-19T04:00:55Z",
      "updated_at": "2026-09-19T04:00:55Z",
      "abstract": "Large Language Models (LLMs) have recently been introduced into traffic signal control (TSC) as decision agents due to their strengths in human-readable reasoning generation. Yet, existing LLM TSC methods optimize only from final outcomes and fail to distinguish valid from flawed reasoning steps, causing useful or misleading steps to be jointly updated and thus impairing the model's learning of effective reasoning. To bridge this gap, we propose an LLM-based framework ProcessLight to decompose signal decisions into verifiable semantic steps. Building on ProcessLight, we further develop Step-wise Traffic Process Policy Optimization (STeP-PO), a novel reinforcement learning framework that optimizes structured reasoning processes through step-level credit assignment. Specifically, STeP-PO uses step quality scores to evaluate local reasoning quality and step importance to measure each step's influence on the final action, and then assigns step-level advantages over a semantic step tree structure. The resulting step-level advantages are propagated to reasoning tokens, enabling fine-grained policy optimization beyond outcome-only rewards. Extensive experiments over multiple real-world datasets demonstrate the superiority of our methods. Our code is available at https://github.com/wenzhaoabc/processlight.",
      "source_url": "https://arxiv.org/abs/2609.22746",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-29T08:39:16Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power-system"
          ]
        },
        "technique_matches": {
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-26812",
      "arxiv_id": "2610.26812",
      "title": "Data Center Oscillation Isolation and Grid Support: Fundamental Limits and Source-Selective Control",
      "summary_ko": "The grid integration of large data centers poses a growing power-system stability challenge. Rapidly varying computing demand can induce oscillations, while the facilities must provide a prescribed response to grid disturbances. Existing controls address load fluctuations and grid support but do not establish their jointly achievable performance. This paper derives fundamental limits and develops a source-selective controller for simultaneous oscillation isolation and grid support. A coupled closed-loop model gives a condition for distinguishing disturbance sources and reveals a direct isolation-support conflict under grid-import-power feedback alone; additional electrical measurements can remove this pointwise conflict but leave a conditional cross-frequency sensitivity limit. Guided by these limits, we design a local residual-feedback storage controller through convex frequency-domain optimization with stability, isolation, and grid-response constraints across operating points. Tests on a single-generator interconnection and a modified nine-bus network show stronger oscillation isolation than equal-order optimized comparators while meeting the prescribed grid response across operating points.",
      "first_seen_at": "2026-10-20T07:44:48Z",
      "discovery_sources": [],
      "authors": [
        "Cheng Feng"
      ],
      "published_at": "2026-10-19T17:50:47Z",
      "updated_at": "2026-10-19T17:50:47Z",
      "abstract": "The grid integration of large data centers poses a growing power-system stability challenge. Rapidly varying computing demand can induce oscillations, while the facilities must provide a prescribed response to grid disturbances. Existing controls address load fluctuations and grid support but do not establish their jointly achievable performance. This paper derives fundamental limits and develops a source-selective controller for simultaneous oscillation isolation and grid support. A coupled closed-loop model gives a condition for distinguishing disturbance sources and reveals a direct isolation-support conflict under grid-import-power feedback alone; additional electrical measurements can remove this pointwise conflict but leave a conditional cross-frequency sensitivity limit. Guided by these limits, we design a local residual-feedback storage controller through convex frequency-domain optimization with stability, isolation, and grid-response constraints across operating points. Tests on a single-generator interconnection and a modified nine-bus network show stronger oscillation isolation than equal-order optimized comparators while meeting the prescribed grid response across operating points.",
      "source_url": "https://arxiv.org/abs/2610.26812",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "제조"
      ],
      "techniques": [
        "AI"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-29T08:39:16Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "제조": [
            "manufacturing"
          ]
        },
        "technique_matches": {
          "AI": [
            "AI",
            "deep learning"
          ]
        }
      },
      "id": "auto-arxiv-2610-34304",
      "arxiv_id": "2610.34304",
      "title": "Real-Time Conveyor Belt Defect Detection via Edge TPU-Accelerated YOLOv11 on Raspberry Pi 5",
      "summary_ko": "Conveyor belts are vital arteries in continuous bulk material handling, particularly in heavy process industries such as cement manufacturing. Undetected structural anomalies (longitudinal tears, holes, degraded patch repairs, and impact punctures) cause catastrophic belt snapping, costly downtime, and severe safety hazards. While deep learning offers high accuracy, existing models rely on power-hungry workstation GPUs, hindering practical deployment at remote industrial edge points. In this paper, we propose, implement, and validate an end-to-end, low-power edge AI vision system for real-time conveyor belt defect detection deployed on a Raspberry Pi 5 accelerated by a Google Coral Edge TPU coprocessor. The system leverages an optimized YOLOv11 nano (YOLOv11n) architecture trained on a multi-source industrial corpus exceeding 3,000 augmented samples from an operational cement plant in West Java, open-source datasets, and automated captures. The model undergoes post-training full integer quantization (INT8) compiled for systolic tensor execution. Experimental results demonstrate robust detection capability, achieving precision of 0.742, recall of 0.730, and mAP@0.5 of 0.764 across four defect classes. Deployed on-site in an IP-rated enclosure interfaced with an RTSP PoE industrial camera, the system achieves stable throughput of 30-40 FPS at 320x320 resolution with an end-to-end latency of 70-90 ms. Operational profiling under harsh clinker dust confirms thermal equilibrium at 55 +/- 5 deg C on the host CPU and ~65 deg C on the Edge TPU with zero frame loss. This demonstrates that edge AI bridges the gap between high-accuracy computer vision and cost-effective predictive maintenance in continuous manufacturing.",
      "first_seen_at": "2026-10-27T02:19:57Z",
      "discovery_sources": [],
      "authors": [
        "Muhammad Nur Bijak Bestari",
        "Mulki Indana Zulfa"
      ],
      "published_at": "2026-10-25T14:14:37Z",
      "updated_at": "2026-10-25T14:14:37Z",
      "abstract": "Conveyor belts are vital arteries in continuous bulk material handling, particularly in heavy process industries such as cement manufacturing. Undetected structural anomalies (longitudinal tears, holes, degraded patch repairs, and impact punctures) cause catastrophic belt snapping, costly downtime, and severe safety hazards. While deep learning offers high accuracy, existing models rely on power-hungry workstation GPUs, hindering practical deployment at remote industrial edge points. In this paper, we propose, implement, and validate an end-to-end, low-power edge AI vision system for real-time conveyor belt defect detection deployed on a Raspberry Pi 5 accelerated by a Google Coral Edge TPU coprocessor. The system leverages an optimized YOLOv11 nano (YOLOv11n) architecture trained on a multi-source industrial corpus exceeding 3,000 augmented samples from an operational cement plant in West Java, open-source datasets, and automated captures. The model undergoes post-training full integer quantization (INT8) compiled for systolic tensor execution. Experimental results demonstrate robust detection capability, achieving precision of 0.742, recall of 0.730, and mAP@0.5 of 0.764 across four defect classes. Deployed on-site in an IP-rated enclosure interfaced with an RTSP PoE industrial camera, the system achieves stable throughput of 30-40 FPS at 320x320 resolution with an end-to-end latency of 70-90 ms. Operational profiling under harsh clinker dust confirms thermal equilibrium at 55 +/- 5 deg C on the host CPU and ~65 deg C on the Edge TPU with zero frame loss. This demonstrates that edge AI bridges the gap between high-accuracy computer vision and cost-effective predictive maintenance in continuous manufacturing.",
      "source_url": "https://arxiv.org/abs/2610.34304",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "AI",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-29T08:39:16Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power systems",
            "renewable energy",
            "power flow"
          ]
        },
        "technique_matches": {
          "AI": [
            "deep learning",
            "neural networks"
          ],
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-26351",
      "arxiv_id": "2610.26351",
      "title": "PhyPro-FM: A Physics and Prompt-Guided Graph Foundation Model for Contingency Analysis",
      "summary_ko": "The continuous evolution of next-generation power systems, characterized by the high penetration of renewable energy resources and dynamic topology variations, poses formidable challenges to traditional N-1/N-2 contingency analysis in terms of computational efficiency and scalability. While deep learning-based methods offer accelerated inference, existing studies predominantly treat contingency analysis as a pure regression problem, often overlooking the physical essence of grid operation and engineering requirements of real-world scenarios. To bridge this gap, this paper proposes a physics and prompt-guided graph foundation model (PhyPro-FM) for generalizable and engineering-viable contingency analysis. The proposed architecture integrates graph neural networks with a learnable global query mechanism and, notably, introduces a prompt-guided learning paradigm. By leveraging historical snapshots as few-shot prompts, the model dynamically captures correlations between target and historical operating conditions, significantly enhancing generalization capabilities under unseen topologies. Furthermore, we design a physics-informed training mechanism that prioritizes branch power flow errors over nodal voltage errors, aligning the optimization objective with critical grid security constraints. To ensure engineering applicability, a physics-informed inference mechanism is also developed, incorporating a residual-based correction module and a specialized post-processing strategy for singular branches to circumvent numerical instability. Comprehensive experiments demonstrate that PhyPro-FM outperforms state-of-the-art methods in accuracy and generalization, validating its superior physical consistency and robustness for practical engineering applications.",
      "first_seen_at": "2026-10-20T07:44:48Z",
      "discovery_sources": [],
      "authors": [
        "Xu Yang",
        "Zhanyuan Sheng",
        "Chenhui Lin",
        "Haotian Liu",
        "Qi Wang",
        "Yue Yang",
        "Wenchuan Wu"
      ],
      "published_at": "2026-10-19T13:16:43Z",
      "updated_at": "2026-10-19T13:16:43Z",
      "abstract": "The continuous evolution of next-generation power systems, characterized by the high penetration of renewable energy resources and dynamic topology variations, poses formidable challenges to traditional N-1/N-2 contingency analysis in terms of computational efficiency and scalability. While deep learning-based methods offer accelerated inference, existing studies predominantly treat contingency analysis as a pure regression problem, often overlooking the physical essence of grid operation and engineering requirements of real-world scenarios. To bridge this gap, this paper proposes a physics and prompt-guided graph foundation model (PhyPro-FM) for generalizable and engineering-viable contingency analysis. The proposed architecture integrates graph neural networks with a learnable global query mechanism and, notably, introduces a prompt-guided learning paradigm. By leveraging historical snapshots as few-shot prompts, the model dynamically captures correlations between target and historical operating conditions, significantly enhancing generalization capabilities under unseen topologies. Furthermore, we design a physics-informed training mechanism that prioritizes branch power flow errors over nodal voltage errors, aligning the optimization objective with critical grid security constraints. To ensure engineering applicability, a physics-informed inference mechanism is also developed, incorporating a residual-based correction module and a specialized post-processing strategy for singular branches to circumvent numerical instability. Comprehensive experiments demonstrate that PhyPro-FM outperforms state-of-the-art methods in accuracy and generalization, validating its superior physical consistency and robustness for practical engineering applications.",
      "source_url": "https://arxiv.org/abs/2610.26351",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "시뮬레이션",
        "데이터 분석"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-29T08:39:16Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "도로교통": [
            "road traffic"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulators"
          ],
          "데이터 분석": [
            "Time Series"
          ]
        }
      },
      "id": "auto-arxiv-2610-26582",
      "arxiv_id": "2610.26582",
      "title": "WhatIf-Bench: Benchmarking Intervention-Aware Time Series Forecasting",
      "summary_ko": "Recent time series foundation models (TSFMs) can consume covariates in-context, extending zero-shot forecasting to problems driven by external factors. This enables scenario analysis: forecasting how a target responds to events that set covariates to chosen future values. Whether they can do this reliably is an open question. We introduce WhatIf-Bench, a benchmark measuring how well forecasters use covariates to forecast a target's response to future events. WhatIf-Bench is semi-synthetic: time series come from mechanistic simulators of data centers, power grids, road traffic, and water networks, with controlled covariate-target relationships grounded in each system. Each task presents a history of events intervening on covariates, then asks to forecast a future event that either replays seen values (Replay) or sets them to unseen ones (Novel). Varying the number of covariates, historical events, and event length yields 37,665 tasks. Evaluating TSFMs, frontier LLMs, and simple regression baselines, we find the LLMs reach the highest skill (improvement over a naive baseline), ahead of the best TSFMs. Every model forecasts well on Replay futures (48 to 85% skill) but drops to -7 to 50% on Novel futures, with the regression baselines falling the most. Skill on Novel futures also falls as events set more covariates jointly. More historical events raise skill on Novel futures across the board, but large differences remain. WhatIf-Bench reveals generalization to Novel futures as the core challenge, and offers a testbed toward arbitrary scenario analysis.",
      "first_seen_at": "2026-10-20T07:44:48Z",
      "discovery_sources": [],
      "authors": [
        "Arjun Ashok",
        "Mihir Parmar",
        "Palash Goyal",
        "Bhavana Dalvi Mishra",
        "Chun-Liang Li",
        "Irina Rish",
        "Alexandre Drouin",
        "Jinsung Yoon",
        "Tomas Pfister"
      ],
      "published_at": "2026-10-19T15:32:53Z",
      "updated_at": "2026-10-21T19:26:44Z",
      "abstract": "Recent time series foundation models (TSFMs) can consume covariates in-context, extending zero-shot forecasting to problems driven by external factors. This enables scenario analysis: forecasting how a target responds to events that set covariates to chosen future values. Whether they can do this reliably is an open question. We introduce WhatIf-Bench, a benchmark measuring how well forecasters use covariates to forecast a target's response to future events. WhatIf-Bench is semi-synthetic: time series come from mechanistic simulators of data centers, power grids, road traffic, and water networks, with controlled covariate-target relationships grounded in each system. Each task presents a history of events intervening on covariates, then asks to forecast a future event that either replays seen values (Replay) or sets them to unseen ones (Novel). Varying the number of covariates, historical events, and event length yields 37,665 tasks. Evaluating TSFMs, frontier LLMs, and simple regression baselines, we find the LLMs reach the highest skill (improvement over a naive baseline), ahead of the best TSFMs. Every model forecasts well on Replay futures (48 to 85% skill) but drops to -7 to 50% on Novel futures, with the regression baselines falling the most. Skill on Novel futures also falls as events set more covariates jointly. More historical events raise skill on Novel futures across the board, but large differences remain. WhatIf-Bench reveals generalization to Novel futures as the core challenge, and offers a testbed toward arbitrary scenario analysis.",
      "source_url": "https://arxiv.org/abs/2610.26582",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "제조"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-29T08:39:16Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "제조": [
            "manufacturing"
          ]
        },
        "technique_matches": {
          "최적화": [
            "Optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-33374",
      "arxiv_id": "2610.33374",
      "title": "An In-depth Study of Stochastic Stellarator Coil Optimization",
      "summary_ko": "We extend stochastic coil optimization for stellarators and analyze the effect of optimizing for coil manufacturing errors, assembly errors represented by centroid translations and angular offsets, and errors in the coil currents. We further analyze the impact of different perturbation methods and use them in conjunction with the Augmented Lagrangian algorithm. For three stellarators, we show that stochastic methods regularize the coil optimization landscape and improve robustness in every case, with the magnitude of the improvement varying substantially across different plasmas. Our metric for coil robustness, $Q$, is improved by $4.50\\%$ to $729\\%$ across different configurations. Lastly, we perform numerous hyperparameter scans, including e.g. scanning the training perturbation size and the out-of-sample (OOS) evaluation perturbation size and find coupled and de-coupled training tolerance gains ranging from $0.09$ mm to $1.35$ mm depending strongly on the particular stellarator.",
      "first_seen_at": "2026-10-27T02:19:57Z",
      "discovery_sources": [],
      "authors": [
        "Vincenzo Gallegos",
        "Evan Laljit",
        "Julianne Stratton",
        "Gabe Perez-Giz",
        "Alan Kaptanoglu"
      ],
      "published_at": "2026-10-24T00:02:10Z",
      "updated_at": "2026-10-24T00:02:10Z",
      "abstract": "We extend stochastic coil optimization for stellarators and analyze the effect of optimizing for coil manufacturing errors, assembly errors represented by centroid translations and angular offsets, and errors in the coil currents. We further analyze the impact of different perturbation methods and use them in conjunction with the Augmented Lagrangian algorithm. For three stellarators, we show that stochastic methods regularize the coil optimization landscape and improve robustness in every case, with the magnitude of the improvement varying substantially across different plasmas. Our metric for coil robustness, $Q$, is improved by $4.50\\%$ to $729\\%$ across different configurations. Lastly, we perform numerous hyperparameter scans, including e.g. scanning the training perturbation size and the out-of-sample (OOS) evaluation perturbation size and find coupled and de-coupled training tolerance gains ranging from $0.09$ mm to $1.35$ mm depending strongly on the particular stellarator.",
      "source_url": "https://arxiv.org/abs/2610.33374",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-30T08:45:56Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "도로교통": [
            "Road traffic",
            "traffic simulation"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2610-26227",
      "arxiv_id": "2610.26227",
      "title": "Who Breathes the Detour? A Coupled Traffic-Emission-Dispersion-Exposure Pipeline",
      "summary_ko": "Road traffic is among the largest sources of health-damaging air pollution in inhabited areas. Direct measurement of resident exposure at the scale of an entire corridor remains costly, and existing simulation approaches often target either high-fidelity local snapshots or regional averages, leaving the corridor scale underserved. We present a coupled, end-to-end pipeline that links four stages: (i) microscopic traffic simulation with SUMO, (ii) per-vehicle emissions from the HBEFA4 model, (iii) deterministic three-dimensional Eulerian diffusion solved with an implicit finite-difference scheme on a geometric vertical grid, and (iv) resident-level exposure expressed as an air quality index over a synthetic population. The design deliberately trades the fidelity of full CFD dispersion for a workflow that runs end-to-end for the residential areas of several settlements on a single laptop, and the complete implementation is released open source. We showcase the pipeline at the example of the Swiss Gotthard transit corridor, covering the residential areas of six villages, two days, four scenarios, and three pollutants, to enable assessment of congestion mitigation measures. Code & Material: https://github.com/cedriczeiter/zeiter_coupled_pipeline.",
      "first_seen_at": "2026-10-20T07:44:48Z",
      "discovery_sources": [],
      "authors": [
        "Cédric-Yséry Zeiter",
        "Kevin Riehl",
        "Anastasios Kouvelas"
      ],
      "published_at": "2026-10-19T11:54:41Z",
      "updated_at": "2026-10-19T11:54:41Z",
      "abstract": "Road traffic is among the largest sources of health-damaging air pollution in inhabited areas. Direct measurement of resident exposure at the scale of an entire corridor remains costly, and existing simulation approaches often target either high-fidelity local snapshots or regional averages, leaving the corridor scale underserved. We present a coupled, end-to-end pipeline that links four stages: (i) microscopic traffic simulation with SUMO, (ii) per-vehicle emissions from the HBEFA4 model, (iii) deterministic three-dimensional Eulerian diffusion solved with an implicit finite-difference scheme on a geometric vertical grid, and (iv) resident-level exposure expressed as an air quality index over a synthetic population. The design deliberately trades the fidelity of full CFD dispersion for a workflow that runs end-to-end for the residential areas of several settlements on a single laptop, and the complete implementation is released open source. We showcase the pipeline at the example of the Swiss Gotthard transit corridor, covering the residential areas of six villages, two days, four scenarios, and three pollutants, to enable assessment of congestion mitigation measures. Code & Material: https://github.com/cedriczeiter/zeiter_coupled_pipeline.",
      "source_url": "https://arxiv.org/abs/2610.26227",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-30T08:45:56Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power-system"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2610-30428",
      "arxiv_id": "2610.30428",
      "title": "Computationally Transparent Power Flexibility Aggregation via Directional Support Evaluation",
      "summary_ko": "The growing penetration of distributed energy resources (DERs) creates new opportunities for improving resource adequacy, operational flexibility, and power-system reliability. However, efficiently characterizing the aggregate flexibility of heterogeneous DER portfolios remains challenging because of high dimensionality, diverse operating dynamics, and spatio-temporal coupling constraints. This paper proposes a computationally transparent framework that formulates aggregate flexibility characterization as a sequence of linear programming (LP)-based directional support evaluations. The framework accommodates heterogeneous DER technologies and configurable device-, feeder-, and portfolio-level constraints. The convex hull of the resulting boundary trajectories provides a feasible inner approximation of the coupled aggregate power flexibility region. Theoretical analysis establishes feasibility and non-overestimation guarantees and characterizes the computational dependence on portfolio size, scheduling horizon, and direction budget. In the small-scale case, the proposed method produces tighter projected regions than box, zonotope, and Chebyshev inner-ball benchmarks and matches a dense directional reference within numerical tolerance using 16--64 construction directions. Large-scale simulations with up to 2000 DERs demonstrate approximately linear empirical runtime scaling with the portfolio size and direction budget over the tested ranges. The results establish a scalable and theoretically grounded approach for aggregate DER flexibility characterization.",
      "first_seen_at": "2026-10-23T07:42:21Z",
      "discovery_sources": [],
      "authors": [
        "Mingyu Zhai",
        "Liang Du",
        "Yue Chen"
      ],
      "published_at": "2026-10-21T20:36:50Z",
      "updated_at": "2026-10-21T20:36:50Z",
      "abstract": "The growing penetration of distributed energy resources (DERs) creates new opportunities for improving resource adequacy, operational flexibility, and power-system reliability. However, efficiently characterizing the aggregate flexibility of heterogeneous DER portfolios remains challenging because of high dimensionality, diverse operating dynamics, and spatio-temporal coupling constraints. This paper proposes a computationally transparent framework that formulates aggregate flexibility characterization as a sequence of linear programming (LP)-based directional support evaluations. The framework accommodates heterogeneous DER technologies and configurable device-, feeder-, and portfolio-level constraints. The convex hull of the resulting boundary trajectories provides a feasible inner approximation of the coupled aggregate power flexibility region. Theoretical analysis establishes feasibility and non-overestimation guarantees and characterizes the computational dependence on portfolio size, scheduling horizon, and direction budget. In the small-scale case, the proposed method produces tighter projected regions than box, zonotope, and Chebyshev inner-ball benchmarks and matches a dense directional reference within numerical tolerance using 16--64 construction directions. Large-scale simulations with up to 2000 DERs demonstrate approximately linear empirical runtime scaling with the portfolio size and direction budget over the tested ranges. The results establish a scalable and theoretically grounded approach for aggregate DER flexibility characterization.",
      "source_url": "https://arxiv.org/abs/2610.30428",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지",
        "건설"
      ],
      "techniques": [
        "시뮬레이션",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-30T08:45:56Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "Energy System",
            "energy systems",
            "building energy"
          ],
          "건설": [
            "building energy"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation",
            "simulations",
            "simulator"
          ],
          "최적화": [
            "Optimization",
            "mixed-integer"
          ]
        }
      },
      "id": "auto-arxiv-2610-35160",
      "arxiv_id": "2610.35160",
      "title": "AutoEnergyDesign: An LLM-Driven Agent for Automatic Energy System Design Optimization",
      "summary_ko": "Designing building energy systems is a high-dimensional, mixed-integer search problem: when each design is evaluated by a year-long simulation, black-box optimizers typically need hundreds of evaluations. This paper presents AutoEnergyDesign, a framework in which a Large Language Model (LLM) agent reasons over previous simulation results, proposes configuration changes and learns in context from each outcome, while a git-based mechanism automatically reverts unsuccessful changes. The agent couples a reasoning model with an explicit memory organisation (procedural, semantic, episodic and reflective), a constrained action interface and an environment-grounded keep/revert signal. The simulator covers eight technologies (photovoltaics, micro-wind, combined heat and power, solar thermal, gas boilers, batteries, heat pumps and inverters) and computes the 25-year Net Present Cost (NPC), including capital and operating costs and salvage value. On a case study with measured building load profiles, the agent, run with Claude Haiku 5.5, Sonnet 5.5 and Opus 5.5 (ten runs of 50 simulations each), is compared with random search, Tree-structured Parzen Estimator (TPE) Bayesian optimization and a $(1+1)$ evolution strategy sharing the agent's acceptance rule (300 evaluations, ten seeds). With all three models the agent reaches an NPC of about 3.53 MEUR, 1.20% below the best TPE result and 0.23% below the best evolution-strategy result. It comes within 1% of this design after a median of 18 (Opus) to 27.5 (Haiku) simulations, whereas among the baselines only the evolution strategy does so, in half of its seeds and after a median of 192 evaluations. Opus reaches the best design in all ten runs, Sonnet in six (two runs never come within 1%), and Haiku comes within 1% in every run but reaches it exactly only once. Every decision is recorded in an auditable trace.",
      "first_seen_at": "2026-10-29T08:39:16Z",
      "discovery_sources": [],
      "authors": [
        "Amedeo Buonanno",
        "Salvatore Fabozzi",
        "Maria Valenti"
      ],
      "published_at": "2026-10-26T10:25:55Z",
      "updated_at": "2026-11-04T15:58:14Z",
      "abstract": "The design optimization of building energy systems represents a complex, high-dimensional search problem involving discrete technology selections, continuous sizing parameters, and intricate economic trade-offs in which every candidate must be evaluated by a year-long simulation. This paper presents AutoEnergyDesign, in which a Large Language Model (LLM) agent reasons over past simulation results, proposes configuration changes and learns in context from each outcome, while a git-based mechanism reverts changes that do not improve the design. The agent couples a reasoning model with explicit memories, a constrained action interface, and an environment-grounded keep/revert feedback signal. The simulator covers eight technologies and computes the 25-year Net Present Cost (NPC). On a case study with measured building loads, the agent, instantiated with three Claude models (Haiku 5.5, Sonnet 5.5 and Opus 5.5; ten runs of 50 simulations each), is compared with random search, Tree-structured Parzen Estimator (TPE) Bayesian optimization and a (1+1) evolution strategy that shares the agent's acceptance rule (ten seeds of 300 evaluations). With all three models the agent reaches an NPC of 3.53MEUR, 1.20% and 0.23% below the best results of TPE and of the evolution strategy, and comes within 1% of it after a median of 18 to 27.5 simulations; among the baselines, only the evolution strategy does so, after a median of 192 evaluations. Every decision is recorded, making the search auditable.",
      "source_url": "https://arxiv.org/abs/2610.35160",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "철도"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-30T08:45:56Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "철도": [
            "railway"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2610-36122",
      "arxiv_id": "2610.36122",
      "title": "Acceleration data-based robust on-board monitoring of hunting proximity in railway vehicles under uncertainty: A feasibility study",
      "summary_ko": "The feasibility of on-board, real-time proximity assessment to railway hunting initiation under uncertainty arising from vehicle/track and other operational conditions is assessed. In this context, a novel method that classifies hunting proximity into one of several prespecified speed proximity intervals is introduced, based on a grey nonlinear vehicle model with parameters estimated from on-board acceleration measurements and model-based estimation of the Maximum Lyapunov Exponent (MLE) as a hunting proximity measure. The method's performance and robustness are assessed via Monte Carlo simulations using a MATLAB-based multi-body vehicle model and four operating scenarios. The method is shown to achieve excellent performance achieving 97.08 % classification accuracy, which marks a significant improvement over a potential alternative. Its robustness to faulty suspensions and hollow-worn wheels is also demonstrated, although certain performance compromise is observed under worn-track conditions.",
      "first_seen_at": "2026-10-29T08:39:16Z",
      "discovery_sources": [],
      "authors": [
        "Kyriakos Kritikakos",
        "Spilios D. Fassois",
        "John S. Sakellariou"
      ],
      "published_at": "2026-10-26T18:31:31Z",
      "updated_at": "2026-10-26T18:31:31Z",
      "abstract": "The feasibility of on-board, real-time proximity assessment to railway hunting initiation under uncertainty arising from vehicle/track and other operational conditions is assessed. In this context, a novel method that classifies hunting proximity into one of several prespecified speed proximity intervals is introduced, based on a grey nonlinear vehicle model with parameters estimated from on-board acceleration measurements and model-based estimation of the Maximum Lyapunov Exponent (MLE) as a hunting proximity measure. The method's performance and robustness are assessed via Monte Carlo simulations using a MATLAB-based multi-body vehicle model and four operating scenarios. The method is shown to achieve excellent performance achieving 97.08 % classification accuracy, which marks a significant improvement over a potential alternative. Its robustness to faulty suspensions and hollow-worn wheels is also demonstrated, although certain performance compromise is observed under worn-track conditions.",
      "source_url": "https://arxiv.org/abs/2610.36122",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "AI"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-30T08:45:56Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "도로교통": [
            "Traffic Signal"
          ]
        },
        "technique_matches": {
          "AI": [
            "Reinforcement Learning"
          ]
        }
      },
      "id": "auto-arxiv-2610-31037",
      "arxiv_id": "2610.31037",
      "title": "Stability-Preserving Reinforcement Learning for Heterogeneity-Aware Composite Max-Pressure Traffic Signal Control",
      "summary_ko": "Reinforcement learning (RL) provides a flexible mechanism for adapting traffic signal control to heterogeneous traffic and sensing conditions, but directly learned signal actions do not automatically inherit the analytical properties of structured traffic-control methods. This paper proposes Com-MP, a theory-guided RL framework that learns a context-dependent composition of multiple max-pressure (MP) control preferences while retaining decentralized MP phase selection. The learned policy adapts the relative influence of constituent MP formulations according to local traffic and sensing conditions, allowing data-driven adaptation to operate within a structured traffic-control framework. We show that such intersection-specific and time-varying composition preserves strong queue stability for every demand in the strictly stabilizable region under any admissible non-anticipative composition policy, thereby separating the performance-adaptation role of learning from the stability property of the underlying controller. In a representative implementation combining detector-supported queue-based MP and connected-vehicle-supported travel-time MP, experiments on a real-world 45-intersection network show that Com-MP reduces average delay by 15.6\\% and 8.6\\% relative to the two constituent controllers, respectively, and by about 7.0\\% relative to the best fixed composition. The learned composition responds systematically to local traffic and sensing conditions, remains effective under heterogeneous and degraded sensing, and can be transferred without retraining to an unseen network with an average delay only 2.4\\% higher than that of a policy trained directly on the target network. These results show how learning-based adaptation can be introduced within a stability-preserving traffic-control structure, providing a theory-guided pathway toward trustworthy learning-enabled traffic signal control.",
      "first_seen_at": "2026-10-23T07:42:21Z",
      "discovery_sources": [],
      "authors": [
        "Jichen Zhu",
        "Chaopeng Tan"
      ],
      "published_at": "2026-10-22T10:16:16Z",
      "updated_at": "2026-10-22T10:16:16Z",
      "abstract": "Reinforcement learning (RL) provides a flexible mechanism for adapting traffic signal control to heterogeneous traffic and sensing conditions, but directly learned signal actions do not automatically inherit the analytical properties of structured traffic-control methods. This paper proposes Com-MP, a theory-guided RL framework that learns a context-dependent composition of multiple max-pressure (MP) control preferences while retaining decentralized MP phase selection. The learned policy adapts the relative influence of constituent MP formulations according to local traffic and sensing conditions, allowing data-driven adaptation to operate within a structured traffic-control framework. We show that such intersection-specific and time-varying composition preserves strong queue stability for every demand in the strictly stabilizable region under any admissible non-anticipative composition policy, thereby separating the performance-adaptation role of learning from the stability property of the underlying controller. In a representative implementation combining detector-supported queue-based MP and connected-vehicle-supported travel-time MP, experiments on a real-world 45-intersection network show that Com-MP reduces average delay by 15.6\\% and 8.6\\% relative to the two constituent controllers, respectively, and by about 7.0\\% relative to the best fixed composition. The learned composition responds systematically to local traffic and sensing conditions, remains effective under heterogeneous and degraded sensing, and can be transferred without retraining to an unseen network with an average delay only 2.4\\% higher than that of a policy trained directly on the target network. These results show how learning-based adaptation can be introduced within a stability-preserving traffic-control structure, providing a theory-guided pathway toward trustworthy learning-enabled traffic signal control.",
      "source_url": "https://arxiv.org/abs/2610.31037",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-30T08:45:56Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power system",
            "power flow"
          ]
        },
        "technique_matches": {
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-32006",
      "arxiv_id": "2610.32006",
      "title": "Strengthening AC optimal power flow relaxation through offline bound tightening and linear approximations",
      "summary_ko": "The alternating-current optimal power flow (ACOPF) problem is fundamental to power system operation and planning, but its nonconvexity makes globally optimal solutions of large-scale instances computationally challenging. Convex relaxations provide tractable lower bounds for assessing solution quality and supporting global optimization, although their effectiveness depends strongly on relaxation strength. This paper proposes an offline convexification and bound tightening framework for constructing strong linear programming (LP) relaxations for families of related ACOPF instances. Rather than repeating the tightening procedure for each instance, we compute variable bounds and second-order cone outer-approximation (SOC-OA) cuts offline using a demand-slack model and reuse them for subsequent instances with the same topology and admissible demand variations. Computational results on PGLib instances show that the fully offline approach achieves an average gap of $4.32\\%$, compared with $3.80\\%$ for the fully online approach, while achieving an average speedup of approximately $272\\times$. The results demonstrate that offline convexification and bound tightening can recover most of the relaxation improvement of online tightening while significantly reducing the computational burden for repeated ACOPF evaluations.",
      "first_seen_at": "2026-10-26T01:32:49Z",
      "discovery_sources": [],
      "authors": [
        "Xiangxin An",
        "Constance Crozier",
        "Santanu S. Dey"
      ],
      "published_at": "2026-10-23T00:07:37Z",
      "updated_at": "2026-10-23T00:07:37Z",
      "abstract": "The alternating-current optimal power flow (ACOPF) problem is fundamental to power system operation and planning, but its nonconvexity makes globally optimal solutions of large-scale instances computationally challenging. Convex relaxations provide tractable lower bounds for assessing solution quality and supporting global optimization, although their effectiveness depends strongly on relaxation strength. This paper proposes an offline convexification and bound tightening framework for constructing strong linear programming (LP) relaxations for families of related ACOPF instances. Rather than repeating the tightening procedure for each instance, we compute variable bounds and second-order cone outer-approximation (SOC-OA) cuts offline using a demand-slack model and reuse them for subsequent instances with the same topology and admissible demand variations. Computational results on PGLib instances show that the fully offline approach achieves an average gap of $4.32\\%$, compared with $3.80\\%$ for the fully online approach, while achieving an average speedup of approximately $272\\times$. The results demonstrate that offline convexification and bound tightening can recover most of the relaxation improvement of online tightening while significantly reducing the computational burden for repeated ACOPF evaluations.",
      "source_url": "https://arxiv.org/abs/2610.32006",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "AI",
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-31T08:33:12Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "Robot",
            "robots"
          ]
        },
        "technique_matches": {
          "AI": [
            "Reinforcement learning"
          ],
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2610-26852",
      "arxiv_id": "2610.26852",
      "title": "CoopUQ: Robust Multi-Robot Social Navigation via Uncertainty-Aware Cooperative Forecasting",
      "summary_ko": "Reinforcement learning enables mobile robots to make fast and scalable navigation decisions in constrained human-shared environments. However, directly extending single-robot approaches to multi-robot settings often fails to ensure safety and efficiency, especially under occlusions and out-of-distribution (OOD) scenarios, where individual robots may overlook potential risks or make inaccurate predictions. Leveraging cooperation effectively for both prediction and navigation remains a major challenge. We propose CoopUQ, a multi-robot social navigation framework that bridges uncertainty-aware cooperative occupancy forecasting and robust decision making. CoopUQ predicts future occupancy grid maps through cross-robot spatiotemporal feature fusion and further leverages forecasts and their uncertainty to guide policy via constrained multi-agent reinforcement learning, where the cost penalizes navigation through regions predicted to be occupied or highly uncertain. This design enables the policy to adapt its risk sensitivity under distribution shifts. Experimental results across multiple Unity3D-based simulation environments with both in-distribution and OOD settings, including diverse layouts, pedestrian densities, and rushing behavior, demonstrate improved prediction performance and safety over representative baselines. Real-robot experiments further demonstrate its robustness across various scenes. Our code and videos are available at https://coopuq.github.io/.",
      "first_seen_at": "2026-10-20T07:44:48Z",
      "discovery_sources": [],
      "authors": [
        "Zehao Wang",
        "Jianpeng Yao",
        "Yuping Wang",
        "Yiping Li",
        "Jiaqi Li",
        "Zhixu Li",
        "Kanghoon Lee",
        "Jinfeng Wang",
        "Cong Liu",
        "Zhaowei Tan",
        "Jinkyoo Park",
        "Jiachen Li"
      ],
      "published_at": "2026-10-19T17:59:49Z",
      "updated_at": "2026-10-19T17:59:49Z",
      "abstract": "Reinforcement learning enables mobile robots to make fast and scalable navigation decisions in constrained human-shared environments. However, directly extending single-robot approaches to multi-robot settings often fails to ensure safety and efficiency, especially under occlusions and out-of-distribution (OOD) scenarios, where individual robots may overlook potential risks or make inaccurate predictions. Leveraging cooperation effectively for both prediction and navigation remains a major challenge. We propose CoopUQ, a multi-robot social navigation framework that bridges uncertainty-aware cooperative occupancy forecasting and robust decision making. CoopUQ predicts future occupancy grid maps through cross-robot spatiotemporal feature fusion and further leverages forecasts and their uncertainty to guide policy via constrained multi-agent reinforcement learning, where the cost penalizes navigation through regions predicted to be occupied or highly uncertain. This design enables the policy to adapt its risk sensitivity under distribution shifts. Experimental results across multiple Unity3D-based simulation environments with both in-distribution and OOD settings, including diverse layouts, pedestrian densities, and rushing behavior, demonstrate improved prediction performance and safety over representative baselines. Real-robot experiments further demonstrate its robustness across various scenes. Our code and videos are available at https://coopuq.github.io/.",
      "source_url": "https://arxiv.org/abs/2610.26852",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-31T08:33:12Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "energy system",
            "Energy Systems"
          ]
        },
        "technique_matches": {
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-32759",
      "arxiv_id": "2610.32759",
      "title": "G-ORACLE: A Fast Geometric Method To Explore All Near-Optimal Designs for Energy Systems",
      "summary_ko": "Modeling to Generate Alternatives (MGA) methods explore the near-optimal solutions of energy system optimization models (ESOMs), i.e., alternative planning pathways whose cost lies within a small predefined slack of the optimum but which can differ substantially in technology composition, regional distribution, and environmental impacts. Among MGA methods, ORACLE uniquely guarantees convergence to the near-optimal set within a targeted tolerance by iteratively refining inner and outer polytope approximations. At each iteration, given a trial point, it solves a problem as hard as the ESOM to find the nearest near-optimal solution to it. However, the trial point is obtained by solving a bilevel problem whose solution time grows rapidly with the number of iterations and eventually dominates the computational cost of the method. To eliminate this bottleneck, we present G-ORACLE, which exploits the fact that an optimal solution of the bilevel problem is always attained at a vertex of the outer approximation. G-ORACLE therefore replaces the bilevel problem with incremental vertex enumeration by the double description method and one convex projection per vertex, while retaining the convergence guarantee of ORACLE. A supporting-hyperplane check further avoids unnecessary recomputation of these projections as the inner approximation grows. We evaluate G-ORACLE on an electricity-only PyPSA-Eur model, exploring the near-optimal space of the installed capacities of six generation and two storage technologies. While ORACLE's bilevel problem exceeds 20 minutes at iteration 64, G-ORACLE performs the trial-point search in at most 14 seconds throughout 240 iterations, leaving the ESOM solves as the only significant computational cost.",
      "first_seen_at": "2026-10-26T07:55:22Z",
      "discovery_sources": [],
      "authors": [
        "Sina Hajikazemi",
        "Florian Steinke"
      ],
      "published_at": "2026-10-23T15:02:24Z",
      "updated_at": "2026-11-05T16:14:11Z",
      "abstract": "Modeling to Generate Alternatives (MGA) methods explore the near-optimal solutions of energy system optimization models (ESOMs), i.e., alternative planning pathways whose cost lies within a small predefined slack of the optimum but which can differ substantially in technology composition, regional distribution, and environmental impacts. Among MGA methods, ORACLE uniquely guarantees convergence to the near-optimal set within a targeted tolerance by iteratively refining inner and outer polytope approximations. At each iteration, given a trial point, it solves a problem as hard as the ESOM to find the nearest near-optimal solution to it. However, the trial point is obtained by solving a bilevel problem whose solution time grows rapidly with the number of iterations and eventually dominates the computational cost of the method. To eliminate this bottleneck, we present G-ORACLE, which exploits the fact that an optimal solution of the bilevel problem is always attained at a vertex of the outer approximation. G-ORACLE therefore replaces the bilevel problem with incremental vertex enumeration and one convex projection per vertex, while retaining the convergence guarantee of ORACLE. A supporting-hyperplane check further avoids unnecessary recomputation of these projections as the inner approximation grows. We evaluate G-ORACLE on an electricity-only PyPSA-Eur model, exploring the near-optimal space of the installed capacities of six generation and two storage technologies. While ORACLE's bilevel problem exceeds 8 hours at iteration 94, G-ORACLE with the beneath-and-beyond method performs the trial-point search in at most 7 seconds throughout 247 iterations, leaving the ESOM solves as the only significant computational cost.",
      "source_url": "https://arxiv.org/abs/2610.32759",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "제조"
      ],
      "techniques": [
        "AI"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-31T08:33:12Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "제조": [
            "manufacturing"
          ]
        },
        "technique_matches": {
          "AI": [
            "deep learning",
            "reinforcement learning",
            "neural networks"
          ]
        }
      },
      "id": "auto-arxiv-2610-40421",
      "arxiv_id": "2610.40421",
      "title": "Uncertainty-aware Feedback Loop Diagnosis: integrating reinforcement learning and conformal prediction for reliable wafer defect classification",
      "summary_ko": "Semiconductor manufacturing is a complex, multi-step process that requires both high precision and low operating costs. Therefore, detecting defects in a high-volume setting is an important component of wafer map analysis for reliable production. However, most state-of-the-art deep learning approaches require a full scan of the surface, which hinders the development of high-throughput inspection systems. To address this problem, we propose Uncertainty-aware Feedback Loop Diagnosis (UFLD), a novel algorithm that employs reinforcement learning for adaptive measurement acquisition and provides a prediction uncertainty level after each step, stopping the process once sufficient information is obtained. Unlike existing methods that rely on ensemble models or auxiliary confidence estimation deep learning models, UFLD utilizes a more parameter-efficient conformal prediction framework to determine the optimal stopping time. The proposed UFLD reduces acquisition time by about $40\\%$ and achieves $98.4\\%$ inspection accuracy, without requiring an additional learned confidence-estimation network. We validated our approach on the MixedWM38 dataset, demonstrating its advantages over existing adaptive acquisition procedures and standard convolutional neural networks.",
      "first_seen_at": "2026-10-30T08:45:56Z",
      "discovery_sources": [],
      "authors": [
        "Aleksandr Dekhovich",
        "Carlas Smith",
        "Oleg Soloviev"
      ],
      "published_at": "2026-10-29T16:45:29Z",
      "updated_at": "2026-10-29T16:45:29Z",
      "abstract": "Semiconductor manufacturing is a complex, multi-step process that requires both high precision and low operating costs. Therefore, detecting defects in a high-volume setting is an important component of wafer map analysis for reliable production. However, most state-of-the-art deep learning approaches require a full scan of the surface, which hinders the development of high-throughput inspection systems. To address this problem, we propose Uncertainty-aware Feedback Loop Diagnosis (UFLD), a novel algorithm that employs reinforcement learning for adaptive measurement acquisition and provides a prediction uncertainty level after each step, stopping the process once sufficient information is obtained. Unlike existing methods that rely on ensemble models or auxiliary confidence estimation deep learning models, UFLD utilizes a more parameter-efficient conformal prediction framework to determine the optimal stopping time. The proposed UFLD reduces acquisition time by about $40\\%$ and achieves $98.4\\%$ inspection accuracy, without requiring an additional learned confidence-estimation network. We validated our approach on the MixedWM38 dataset, demonstrating its advantages over existing adaptive acquisition procedures and standard convolutional neural networks.",
      "source_url": "https://arxiv.org/abs/2610.40421",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "물류"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-31T08:33:12Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "물류": [
            "logistics"
          ]
        },
        "technique_matches": {
          "최적화": [
            "optimisation"
          ]
        }
      },
      "id": "auto-arxiv-2610-40129",
      "arxiv_id": "2610.40129",
      "title": "The Generalised Kessler Syndrome and the Persian Gulf",
      "summary_ko": "The term Kessler syndrome was coined for a runaway orbital cascade creating a high-density debris field. The idea is generalised and applied to supply chains. An additive channel enables failures to generate further failures; a subtractive channel erodes the capacity to contain and restore. When damage outpaces repair, failure becomes irreversible and the system settles into a new state. Supplies connect the theory to its application. Stocks insulate subsystems from one another, and their exhaustion exposes dependencies. For the Persian Gulf states, restrictions on maritime supplies from spring 2026 are inadequately compensated by air and road transport alternatives. This depletes inventories unevenly across products and impairs the functioning of the logistics system itself. Throughput optimisation builds fragility everywhere; the Gulf is where it is becoming visible, with the Strait of Hormuz restriction as the trigger. Formulated as a Kelly portfolio allocation under a weakest-link payoff, the optimal strategy concentrates resources on the lagging rather than the leading inputs.",
      "first_seen_at": "2026-10-30T08:45:56Z",
      "discovery_sources": [],
      "authors": [
        "Bernhard K Meister"
      ],
      "published_at": "2026-10-29T14:21:42Z",
      "updated_at": "2026-10-29T14:21:42Z",
      "abstract": "The term Kessler syndrome was coined for a runaway orbital cascade creating a high-density debris field. The idea is generalised and applied to supply chains. An additive channel enables failures to generate further failures; a subtractive channel erodes the capacity to contain and restore. When damage outpaces repair, failure becomes irreversible and the system settles into a new state. Supplies connect the theory to its application. Stocks insulate subsystems from one another, and their exhaustion exposes dependencies. For the Persian Gulf states, restrictions on maritime supplies from spring 2026 are inadequately compensated by air and road transport alternatives. This depletes inventories unevenly across products and impairs the functioning of the logistics system itself. Throughput optimisation builds fragility everywhere; the Gulf is where it is becoming visible, with the Strait of Hormuz restriction as the trigger. Formulated as a Kelly portfolio allocation under a weakest-link payoff, the optimal strategy concentrates resources on the lagging rather than the leading inputs.",
      "source_url": "https://arxiv.org/abs/2610.40129",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-31T08:33:12Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "도로교통": [
            "Traffic Signal"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "Simulator"
          ]
        }
      },
      "id": "auto-arxiv-2610-39320",
      "arxiv_id": "2610.39320",
      "title": "BEVLight: Generalizable Traffic Signal Control from UAV Bird's-Eye Views",
      "summary_ko": "Learning-based traffic signal control (TSC) has achieved promising performance, but most methods rely on structured traffic states that must be estimated from sensor observations in real deployments. Visual TSC learns signal decisions directly from images, yet existing methods depend on fixed roadside cameras and remain tied to specific intersection configurations and visual domains. UAVs offer on-demand bird's-eye-view (BEV) observations that jointly capture intersection topology and traffic dynamics, making them a promising sensing platform for visual TSC. We present BEVLight, a generalizable TSC framework that directly maps BEV observations to signal actions. BEVLight grounds visual features onto individual lanes and composes them along the junction topology into movement and phase representations, allowing a shared policy to score candidate phases across heterogeneous intersection layouts and signal plans. To train the policy, we develop a web-based virtual teleoperation platform and collect over 5K human control decisions together with their underlying traffic states. Simulator-provided traffic states first supervise lane- and movement-level representations, after which human phase selections and rankings supervise the phase scorer. Re-rendering the recorded trajectories under diverse appearances preserves both forms of supervision and encourages robustness to visual changes. Closed-loop experiments demonstrate zero-shot generalization across traffic demands, signal plans, and intersections: BEVLight reduces average waiting time by 7--9\\% relative to perception-to-control visual baselines and matches or outperforms structured-state RL controllers that receive ground-truth traffic states, achieving the lowest waiting time in 12 of 16 scenarios. An offline study further shows that BEVLight transfers to real UAV observations without target-domain adaptation.",
      "first_seen_at": "2026-10-30T08:45:56Z",
      "discovery_sources": [],
      "authors": [
        "Maonan Wang",
        "Yuxin Cai",
        "Junming Liu",
        "Zhengyan Huang",
        "Yi Yu",
        "Ding Wang",
        "Yifeng Zhang",
        "Yirong Chen"
      ],
      "published_at": "2026-10-29T04:03:19Z",
      "updated_at": "2026-10-29T04:03:19Z",
      "abstract": "Learning-based traffic signal control (TSC) has achieved promising performance, but most methods rely on structured traffic states that must be estimated from sensor observations in real deployments. Visual TSC learns signal decisions directly from images, yet existing methods depend on fixed roadside cameras and remain tied to specific intersection configurations and visual domains. UAVs offer on-demand bird's-eye-view (BEV) observations that jointly capture intersection topology and traffic dynamics, making them a promising sensing platform for visual TSC. We present BEVLight, a generalizable TSC framework that directly maps BEV observations to signal actions. BEVLight grounds visual features onto individual lanes and composes them along the junction topology into movement and phase representations, allowing a shared policy to score candidate phases across heterogeneous intersection layouts and signal plans. To train the policy, we develop a web-based virtual teleoperation platform and collect over 5K human control decisions together with their underlying traffic states. Simulator-provided traffic states first supervise lane- and movement-level representations, after which human phase selections and rankings supervise the phase scorer. Re-rendering the recorded trajectories under diverse appearances preserves both forms of supervision and encourages robustness to visual changes. Closed-loop experiments demonstrate zero-shot generalization across traffic demands, signal plans, and intersections: BEVLight reduces average waiting time by 7--9\\% relative to perception-to-control visual baselines and matches or outperforms structured-state RL controllers that receive ground-truth traffic states, achieving the lowest waiting time in 12 of 16 scenarios. An offline study further shows that BEVLight transfers to real UAV observations without target-domain adaptation.",
      "source_url": "https://arxiv.org/abs/2610.39320",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "로봇",
        "토목공사"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-10-31T08:33:12Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "Robot",
            "robots",
            "quadruped"
          ],
          "토목공사": [
            "Civil Engineering"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulator"
          ]
        }
      },
      "id": "auto-arxiv-2610-38141",
      "arxiv_id": "2610.38141",
      "title": "SKiD-SLAM: Resource-Aware Distributed Multi-Robot LiDAR SLAM for Planetary Missions with Field Deployments at Analog Sites",
      "summary_ko": "Future planetary exploration missions require teams of robots to collaboratively map unknown environments under severe communication limits, constrained onboard computation, and no central infrastructure. In this field report, we present SKiD-SLAM, a distributed multi-robot LiDAR simultaneous localization and mapping (SLAM) system for such environments, and report its deployment across two mission concepts: cave exploration and scientific surface exploration. We survey the communication and computation budgets of actual planetary missions and select modules that fit these budgets for inter-robot place recognition, relative pose estimation, and distributed trajectory fusion. Before field deployment, we verify the assembled system on public datasets resembling planetary environments and in a simulator built on NASA JPL's Open Source Rover. We then validate SKiD-SLAM through field deployments in South Korea: a team of wheeled rovers explores the Majang lava-tube cave on Jeju Island, and quadruped robots carry out a crater-and-rock science scenario on a planetary analog terrain at the Korea Institute of Civil Engineering and Building Technology (KICT) in Yeoncheon. Across both deployments, SKiD-SLAM reliably performed distributed multi-robot LiDAR SLAM within a 250 kbps inter-robot communication budget, demonstrating its feasibility under challenging field conditions. Supplementary materials, including code and field data, are available at https://sparolab.github.io/research/skid_slam/.",
      "doi": "10.1109/tfr.2026.3721725",
      "first_seen_at": "2026-10-30T08:45:56Z",
      "discovery_sources": [],
      "authors": [
        "Hogyun Kim",
        "Juwon Kim",
        "Dongjin Cho",
        "Jiwon Choi",
        "Geonmo Yang",
        "Seokhwan Jeong",
        "Jiyun Lee",
        "Miryeong Park",
        "Hyungtae Lim",
        "Younggun Cho"
      ],
      "published_at": "2026-10-28T01:24:37Z",
      "updated_at": "2026-10-28T01:24:37Z",
      "abstract": "Future planetary exploration missions require teams of robots to collaboratively map unknown environments under severe communication limits, constrained onboard computation, and no central infrastructure. In this field report, we present SKiD-SLAM, a distributed multi-robot LiDAR simultaneous localization and mapping (SLAM) system for such environments, and report its deployment across two mission concepts: cave exploration and scientific surface exploration. We survey the communication and computation budgets of actual planetary missions and select modules that fit these budgets for inter-robot place recognition, relative pose estimation, and distributed trajectory fusion. Before field deployment, we verify the assembled system on public datasets resembling planetary environments and in a simulator built on NASA JPL's Open Source Rover. We then validate SKiD-SLAM through field deployments in South Korea: a team of wheeled rovers explores the Majang lava-tube cave on Jeju Island, and quadruped robots carry out a crater-and-rock science scenario on a planetary analog terrain at the Korea Institute of Civil Engineering and Building Technology (KICT) in Yeoncheon. Across both deployments, SKiD-SLAM reliably performed distributed multi-robot LiDAR SLAM within a 250 kbps inter-robot communication budget, demonstrating its feasibility under challenging field conditions. Supplementary materials, including code and field data, are available at https://sparolab.github.io/research/skid_slam/.",
      "source_url": "https://arxiv.org/abs/2610.38141",
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "AI"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-01T09:24:14Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robots"
          ]
        },
        "technique_matches": {
          "AI": [
            "reinforcement learning"
          ]
        }
      },
      "id": "auto-arxiv-2610-26487",
      "arxiv_id": "2610.26487",
      "title": "SPAR: Structural Pruning and Action Recovery for Vision-Language-Action Models",
      "summary_ko": "The limited onboard computing resources of robots make the inference demands of vision-language-action (VLA) models costly to accommodate. Reducing these demands through aggressive compression, however, can degrade manipulation performance beyond what supervised fine-tuning alone recovers. We present SPAR (Structural Pruning and Action Recovery), which simultaneously prunes the hidden and feed-forward intermediate dimensions of the large language model (LLM) backbone through pruning-aware tuning, then applies reinforcement learning (RL) to recover policy performance. Applied to $π_{0.5}$, SPAR reduces the language backbone from 2.34B to 0.09B parameters and the full model from 3.37B to 1.12B, removing approximately 96% and 67% of their parameters, respectively. At this compression level, RL raises average success across the four LIBERO suites from 84.55% to 93.45%, an 8.90-percentage-point gain. The compressed policy achieves a $1.98\\times$ full-policy inference speedup over the unpruned model. These results identify substantial intermediate-dimension redundancy and show that post-pruning RL can recover manipulation performance in compact policies.",
      "first_seen_at": "2026-10-20T07:44:48Z",
      "discovery_sources": [],
      "authors": [
        "Shenli Zheng",
        "Zhou Fang",
        "Yuan Du",
        "Yijiang Liu",
        "Li Du"
      ],
      "published_at": "2026-10-19T14:28:51Z",
      "updated_at": "2026-10-19T14:28:51Z",
      "abstract": "The limited onboard computing resources of robots make the inference demands of vision-language-action (VLA) models costly to accommodate. Reducing these demands through aggressive compression, however, can degrade manipulation performance beyond what supervised fine-tuning alone recovers. We present SPAR (Structural Pruning and Action Recovery), which simultaneously prunes the hidden and feed-forward intermediate dimensions of the large language model (LLM) backbone through pruning-aware tuning, then applies reinforcement learning (RL) to recover policy performance. Applied to $π_{0.5}$, SPAR reduces the language backbone from 2.34B to 0.09B parameters and the full model from 3.37B to 1.12B, removing approximately 96% and 67% of their parameters, respectively. At this compression level, RL raises average success across the four LIBERO suites from 84.55% to 93.45%, an 8.90-percentage-point gain. The compressed policy achieves a $1.98\\times$ full-policy inference speedup over the unpruned model. These results identify substantial intermediate-dimension redundancy and show that post-pruning RL can recover manipulation performance in compact policies.",
      "source_url": "https://arxiv.org/abs/2610.26487",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "우주"
      ],
      "techniques": [
        "시뮬레이션",
        "디지털트윈"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-01T09:24:14Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "우주": [
            "satellite"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation",
            "simulations"
          ],
          "디지털트윈": [
            "Digital Twin"
          ]
        }
      },
      "id": "auto-arxiv-2610-25917",
      "arxiv_id": "2610.25917",
      "title": "An Event-Driven System Architecture for a 15-Minute Urban Flood Digital Twin: A Hanoi Case Study",
      "summary_ko": "Flash floods in Hanoi submerge roads within minutes of heavy rain onset, yet public flood advisories remain retrospective. Hydrodynamic models could supply forward-looking depth guidance, but they are typically run offline for historical events, and prior studies have not evaluated whether physics-based simulation, spatial serving, and vehicle-aware routing can operate together within a fixed refresh cycle using a cloud solver and a single commodity server. This paper evaluates an end-to-end prototype of an automated flood digital twin designed to meet a 15-minute refresh cycle under data scarcity. The architecture organizes execution into decoupled, event-driven workflows that chain weather-gated 3Di simulations through hydraulic restart states, serve flood depths through a dual-track spatial layer, and publish road-edge depths via standardized output contracts. Evaluated on a 57-run storm replay in the My Dinh and Tay Mo sector of Hanoi driven by satellite rainfall estimates, every cycle completed within the 15-minute deadline, publishing a ready edge table at a median of 537 s after slot start. Indexed raster tiles resolved point-depth lookups in a median of 0.54 ms (277 times faster than un-tiled rasters), while pre-rendered vector tiles served web maps without database queries. These findings demonstrate that a commodity server coupled to a cloud solver can sustain the 15-minute operational cycle and decouple routing policies from simulation internals. Because simulated depths were not validated against field gauges and the replay used observed satellite rainfall, the results establish system feasibility rather than forecast accuracy or route safety.",
      "first_seen_at": "2026-10-20T07:44:48Z",
      "discovery_sources": [],
      "authors": [
        "Mai Anh Tran",
        "Vy Nguyen",
        "Vova Nguyen"
      ],
      "published_at": "2026-10-19T07:19:22Z",
      "updated_at": "2026-10-19T07:19:22Z",
      "abstract": "Flash floods in Hanoi submerge roads within minutes of heavy rain onset, yet public flood advisories remain retrospective. Hydrodynamic models could supply forward-looking depth guidance, but they are typically run offline for historical events, and prior studies have not evaluated whether physics-based simulation, spatial serving, and vehicle-aware routing can operate together within a fixed refresh cycle using a cloud solver and a single commodity server. This paper evaluates an end-to-end prototype of an automated flood digital twin designed to meet a 15-minute refresh cycle under data scarcity. The architecture organizes execution into decoupled, event-driven workflows that chain weather-gated 3Di simulations through hydraulic restart states, serve flood depths through a dual-track spatial layer, and publish road-edge depths via standardized output contracts. Evaluated on a 57-run storm replay in the My Dinh and Tay Mo sector of Hanoi driven by satellite rainfall estimates, every cycle completed within the 15-minute deadline, publishing a ready edge table at a median of 537 s after slot start. Indexed raster tiles resolved point-depth lookups in a median of 0.54 ms (277 times faster than un-tiled rasters), while pre-rendered vector tiles served web maps without database queries. These findings demonstrate that a commodity server coupled to a cloud solver can sustain the 15-minute operational cycle and decouple routing policies from simulation internals. Because simulated depths were not validated against field gauges and the replay used observed satellite rainfall, the results establish system feasibility rather than forecast accuracy or route safety.",
      "source_url": "https://arxiv.org/abs/2610.25917",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "로봇",
        "제조"
      ],
      "techniques": [
        "AI",
        "시뮬레이션",
        "데이터 분석"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-01T09:24:14Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robots"
          ],
          "제조": [
            "manufacturing"
          ]
        },
        "technique_matches": {
          "AI": [
            "imitation-learning"
          ],
          "시뮬레이션": [
            "simulation"
          ],
          "데이터 분석": [
            "point cloud",
            "system identification"
          ]
        }
      },
      "id": "auto-arxiv-2610-24235",
      "arxiv_id": "2610.24235",
      "title": "Multimodal Interaction Estimation for Contact-Aware Adaptive Impedance Control",
      "summary_ko": "With advancements in learning-based policies, robots are increasingly being deployed in human-centric environments, such as manufacturing floors and homes, where safe and precise operation requires compensating for unknown loads while remaining compliant during human interactions. Reliably distinguishing between these interaction types is challenging with a single sensing modality, as force signals cannot separate payloads from human pushes, and vision is susceptible to occlusions. We propose a task-agnostic interaction estimation and adaptive control framework that addresses this challenge by fusing force history and 3D point cloud through a transformer-based classifier. Trained with modality dropout for occlusion resilience and fine-tuned from simulation to hardware, the classifier achieves 91.89% accuracy and maintains above 90% under complete occlusion of one modality. The classifier drives a system identification framework that selectively compensates for unknown payloads and a variable-impedance controller that switches between adaptive and reactive modes, while simultaneously estimating interaction forces for downstream tasks, such as human intent estimation. We validate the framework under external perturbations and unknown loads across single-arm manipulation, deployment with an imitation-learning-based policy, intent-driven target switching, and zero-shot bimanual deployment on two Franka arms lifting a box of unknown mass, demonstrating generalization to closed kinematic chains and unseen setups, enabling safe and precise deployment.",
      "first_seen_at": "2026-10-20T07:44:48Z",
      "discovery_sources": [],
      "authors": [
        "Mateusz Jaszczuk",
        "Paris Perdikaris",
        "Nadia Figueroa"
      ],
      "published_at": "2026-10-16T21:00:39Z",
      "updated_at": "2026-10-16T21:00:39Z",
      "abstract": "With advancements in learning-based policies, robots are increasingly being deployed in human-centric environments, such as manufacturing floors and homes, where safe and precise operation requires compensating for unknown loads while remaining compliant during human interactions. Reliably distinguishing between these interaction types is challenging with a single sensing modality, as force signals cannot separate payloads from human pushes, and vision is susceptible to occlusions. We propose a task-agnostic interaction estimation and adaptive control framework that addresses this challenge by fusing force history and 3D point cloud through a transformer-based classifier. Trained with modality dropout for occlusion resilience and fine-tuned from simulation to hardware, the classifier achieves 91.89% accuracy and maintains above 90% under complete occlusion of one modality. The classifier drives a system identification framework that selectively compensates for unknown payloads and a variable-impedance controller that switches between adaptive and reactive modes, while simultaneously estimating interaction forces for downstream tasks, such as human intent estimation. We validate the framework under external perturbations and unknown loads across single-arm manipulation, deployment with an imitation-learning-based policy, intent-driven target switching, and zero-shot bimanual deployment on two Franka arms lifting a box of unknown mass, demonstrating generalization to closed kinematic chains and unseen setups, enabling safe and precise deployment.",
      "source_url": "https://arxiv.org/abs/2610.24235",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "AI"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-01T09:24:14Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power system",
            "Power Grid",
            "power-flow"
          ]
        },
        "technique_matches": {
          "AI": [
            "Neural Networks"
          ]
        }
      },
      "id": "auto-arxiv-2610-32696",
      "arxiv_id": "2610.32696",
      "title": "Foundation Models Meet Power Grid Topology for Cascade Prediction",
      "summary_ko": "Accurate early-stage prediction of cascading failure severity is essential for assessing the vulnerability of power transmission networks. However, existing data-driven approaches often rely on network-specific representations, limiting their generalizability across power system topologies. This work investigates the complementary capabilities of Large Language Models (LLMs) and Graph Neural Networks (GNNs) for cascading failure severity prediction. First, standalone LLM predictors are developed using a LoRA-fine-tuned LLaMA-3-8B-Instruct model with varying degrees of topological information. A hybrid Graph-LLM fusion framework is then proposed, integrating a Topology-Aware Graph Attention Transformer (TAGAT) with the LLM through confidence-gated fusion. TAGAT captures structural dependencies among transmission lines, while the LLM extracts representations from early-stage power-flow observations and aggregated cascade descriptors. The frameworks are evaluated on 30,000 simulated cascading failure scenarios for each of the IEEE 118-bus and IEEE 300-bus systems. Using only the first four cascade steps, the hybrid framework achieves weighted F1 scores of 0.94 and 0.92, respectively. In cross-system transfer from IEEE 118-bus to IEEE 300-bus, it achieves 0.72 zero-shot accuracy, improving to 0.83 with limited target-system calibration, outperforming standalone models and conventional baselines. These results show the potential of combining graph-based structural learning with foundation models to improve cascading failure severity prediction and cross-system transferability.",
      "first_seen_at": "2026-10-26T07:55:22Z",
      "discovery_sources": [],
      "authors": [
        "Soumav Biswas",
        "Mia Naeini"
      ],
      "published_at": "2026-10-23T14:21:47Z",
      "updated_at": "2026-10-23T14:21:47Z",
      "abstract": "Accurate early-stage prediction of cascading failure severity is essential for assessing the vulnerability of power transmission networks. However, existing data-driven approaches often rely on network-specific representations, limiting their generalizability across power system topologies. This work investigates the complementary capabilities of Large Language Models (LLMs) and Graph Neural Networks (GNNs) for cascading failure severity prediction. First, standalone LLM predictors are developed using a LoRA-fine-tuned LLaMA-3-8B-Instruct model with varying degrees of topological information. A hybrid Graph-LLM fusion framework is then proposed, integrating a Topology-Aware Graph Attention Transformer (TAGAT) with the LLM through confidence-gated fusion. TAGAT captures structural dependencies among transmission lines, while the LLM extracts representations from early-stage power-flow observations and aggregated cascade descriptors. The frameworks are evaluated on 30,000 simulated cascading failure scenarios for each of the IEEE 118-bus and IEEE 300-bus systems. Using only the first four cascade steps, the hybrid framework achieves weighted F1 scores of 0.94 and 0.92, respectively. In cross-system transfer from IEEE 118-bus to IEEE 300-bus, it achieves 0.72 zero-shot accuracy, improving to 0.83 with limited target-system calibration, outperforming standalone models and conventional baselines. These results show the potential of combining graph-based structural learning with foundation models to improve cascading failure severity prediction and cross-system transferability.",
      "source_url": "https://arxiv.org/abs/2610.32696",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "물류"
      ],
      "techniques": [
        "AI",
        "시뮬레이션",
        "디지털트윈",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-01T09:24:14Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "물류": [
            "Logistics"
          ]
        },
        "technique_matches": {
          "AI": [
            "AI",
            "reinforcement learning"
          ],
          "시뮬레이션": [
            "simulation"
          ],
          "디지털트윈": [
            "Digital Twin"
          ],
          "최적화": [
            "Optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-38101",
      "arxiv_id": "2610.38101",
      "title": "AI-Driven Digital Twin Framework for Warehouse Logistics Automation Optimization",
      "summary_ko": "In recent years, warehouse logistics optimization has increasingly focused on intelligent, data-driven systems to enhance operational efficiency. Conventional static product placement strategies are being progressively replaced by dynamic slotting approaches that can adapt to real-time variations in warehouse conditions. In this work, we present a novel framework that employs a reinforcement learning agent integrated within a FlexSim-based Digital Twin environment for real-time product placement optimization. The proposed agent continuously learns from streaming operational data, enabling it to reduce picking time while improving overall system throughput. The simulation environment models a four-zone warehouse layout with forklift-based material handling and leverages the Asset Administration Shell framework to ensure seamless interaction between the learning agent, physical asset, and the digital twin. Experimental evaluation demonstrates substantial performance gains, with the proposed approach achieving a 29.66\\% reduction in average picking time, a 35.42\\% increase in throughput, and an 18.54\\% decrease in travel distance.",
      "first_seen_at": "2026-10-30T08:45:56Z",
      "discovery_sources": [],
      "authors": [
        "Md Mahinur Alam",
        "Kanita Jerin Tanha",
        "Md Tanjin Alam",
        "Shadman Sakib",
        "Min Gyu Kim"
      ],
      "published_at": "2026-10-28T00:37:21Z",
      "updated_at": "2026-10-28T00:37:21Z",
      "abstract": "In recent years, warehouse logistics optimization has increasingly focused on intelligent, data-driven systems to enhance operational efficiency. Conventional static product placement strategies are being progressively replaced by dynamic slotting approaches that can adapt to real-time variations in warehouse conditions. In this work, we present a novel framework that employs a reinforcement learning agent integrated within a FlexSim-based Digital Twin environment for real-time product placement optimization. The proposed agent continuously learns from streaming operational data, enabling it to reduce picking time while improving overall system throughput. The simulation environment models a four-zone warehouse layout with forklift-based material handling and leverages the Asset Administration Shell framework to ensure seamless interaction between the learning agent, physical asset, and the digital twin. Experimental evaluation demonstrates substantial performance gains, with the proposed approach achieving a 29.66\\% reduction in average picking time, a 35.42\\% increase in throughput, and an 18.54\\% decrease in travel distance.",
      "source_url": "https://arxiv.org/abs/2610.38101",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "AI"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-01T09:24:14Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "도로교통": [
            "Traffic-Signal"
          ]
        },
        "technique_matches": {
          "AI": [
            "reinforcement learning"
          ]
        }
      },
      "id": "auto-arxiv-2610-38557",
      "arxiv_id": "2610.38557",
      "title": "A Minimally Informed Black-Box Fake-Report Attack on DRL Traffic-Signal Control",
      "summary_ko": "Deep reinforcement learning (DRL) traffic-signal controllers may use vehicle-to-infrastructure (V2I) reports, exposing their perception layer to false-data injection. Existing demonstrations typically assume either white-box access to the controller's weights or full observability of the traffic state. We study the impact of a weaker and more realistic adversary who has no access to the RL model and whose traffic observations are limited to her own approach. This black-box, minimally informed attacker trains an imitation surrogate of the controller. She then crafts bounded fake reports, restricted to inflating vehicle counts on her own approach, that transfer to the real controller. We demonstrate that despite the loss of accuracy relative to an attacker who can observe the entire state and action space, this limited attacker can significantly increase the average waiting time for other approaches while barely changing the waiting time on her own approach, i.e. externalizing the added delay onto the other approaches. Because the only realizable move is to inflate one's own reported queue, we find that a zero-knowledge attacker that simply reports the maximum feasible queue is as damaging as the surrogate-based one, while an unstructured random inflation is not: the surrogate is not required to reproduce the degradation, and the exposed vulnerability is the V2I sensing channel itself. The surrogate's role is directional control, selecting a disruptive or a selfish objective at the same budget. The attack persists under +/-20% uniform demand variation. Finally, on a common per-lane budget we place the attacker on a knowledge ladder against white-box adversaries, and find the black-box surrogate matches a realizable white-box attacker that uses the victim's own gradients; only abandoning realizability raises the damage further.",
      "first_seen_at": "2026-10-30T08:45:56Z",
      "discovery_sources": [],
      "authors": [
        "Amro Saleh",
        "Loukas Lazos",
        "Mohammad J. Abdel-Rahman"
      ],
      "published_at": "2026-10-28T09:53:39Z",
      "updated_at": "2026-10-28T09:53:39Z",
      "abstract": "Deep reinforcement learning (DRL) traffic-signal controllers may use vehicle-to-infrastructure (V2I) reports, exposing their perception layer to false-data injection. Existing demonstrations typically assume either white-box access to the controller's weights or full observability of the traffic state. We study the impact of a weaker and more realistic adversary who has no access to the RL model and whose traffic observations are limited to her own approach. This black-box, minimally informed attacker trains an imitation surrogate of the controller. She then crafts bounded fake reports, restricted to inflating vehicle counts on her own approach, that transfer to the real controller. We demonstrate that despite the loss of accuracy relative to an attacker who can observe the entire state and action space, this limited attacker can significantly increase the average waiting time for other approaches while barely changing the waiting time on her own approach, i.e. externalizing the added delay onto the other approaches. Because the only realizable move is to inflate one's own reported queue, we find that a zero-knowledge attacker that simply reports the maximum feasible queue is as damaging as the surrogate-based one, while an unstructured random inflation is not: the surrogate is not required to reproduce the degradation, and the exposed vulnerability is the V2I sensing channel itself. The surrogate's role is directional control, selecting a disruptive or a selfish objective at the same budget. The attack persists under +/-20% uniform demand variation. Finally, on a common per-lane budget we place the attacker on a knowledge ladder against white-box adversaries, and find the black-box surrogate matches a realizable white-box attacker that uses the victim's own gradients; only abandoning realizability raises the damage further.",
      "source_url": "https://arxiv.org/abs/2610.38557",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "AI",
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-02T09:39:37Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "energy-system",
            "energy systems"
          ]
        },
        "technique_matches": {
          "AI": [
            "AI",
            "Artificial intelligence",
            "artificial-intelligence"
          ],
          "시뮬레이션": [
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2610-35088",
      "arxiv_id": "2610.35088",
      "title": "Moving bits instead of watts: Accelerating global AI data-centre deployment at the energy-digital-water-land nexus",
      "summary_ko": "Artificial intelligence is driving an unprecedented expansion of data centres. However, this growth is increasingly stalled by grid transmission constraints that trap new data centres in years-long connection queues. Unlike electricity, computation can be moved across the globe through fibre-optic networks, but whether this spatial transfer can bypass constrained grids and accelerate global AI data-centre deployment remains unknown. Here we develop a global framework that places future data-centre capacity in energy-abundant regions and routes computational workloads through fibre-optic networks rather than transmitting electricity. We evaluate the framework through more than 11 million hourly operation simulations at subnational resolution, integrating constraints on energy systems, water, digital connectivity, land and financing risk. Results show that across multiple data-centre development and climate-policy scenarios, leveraging global data transfer allows new data centres to be sited where these conditions are favourable, lowering construction costs by 44-47% and carbon emissions by 38%. By treating the global fibre-optic network as an energy-system asset, it avoids 34-67 GW of high-voltage transmission capacity, comparable in magnitude to Italy's national peak electricity demand. Crucially, this methodology could bring more than 95% of connection-delayed data centres online in 2030, with Canada, the Nordic countries and the Arabian Peninsula emerging as preferred locations for data-centre development. Our findings establish computational mobility as an alternative to energy-system expansion, informing the coordinated global development of energy and artificial-intelligence infrastructure.",
      "first_seen_at": "2026-10-27T02:19:57Z",
      "discovery_sources": [],
      "authors": [
        "Jiawei Qu",
        "Xiaoying Shi",
        "Kai Hou",
        "Hongjie Jia",
        "Jinyue Yan"
      ],
      "published_at": "2026-10-26T09:30:16Z",
      "updated_at": "2026-10-26T09:30:16Z",
      "abstract": "Artificial intelligence is driving an unprecedented expansion of data centres. However, this growth is increasingly stalled by grid transmission constraints that trap new data centres in years-long connection queues. Unlike electricity, computation can be moved across the globe through fibre-optic networks, but whether this spatial transfer can bypass constrained grids and accelerate global AI data-centre deployment remains unknown. Here we develop a global framework that places future data-centre capacity in energy-abundant regions and routes computational workloads through fibre-optic networks rather than transmitting electricity. We evaluate the framework through more than 11 million hourly operation simulations at subnational resolution, integrating constraints on energy systems, water, digital connectivity, land and financing risk. Results show that across multiple data-centre development and climate-policy scenarios, leveraging global data transfer allows new data centres to be sited where these conditions are favourable, lowering construction costs by 44-47% and carbon emissions by 38%. By treating the global fibre-optic network as an energy-system asset, it avoids 34-67 GW of high-voltage transmission capacity, comparable in magnitude to Italy's national peak electricity demand. Crucially, this methodology could bring more than 95% of connection-delayed data centres online in 2030, with Canada, the Nordic countries and the Arabian Peninsula emerging as preferred locations for data-centre development. Our findings establish computational mobility as an alternative to energy-system expansion, informing the coordinated global development of energy and artificial-intelligence infrastructure.",
      "source_url": "https://arxiv.org/abs/2610.35088",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "제조"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-02T09:39:37Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "제조": [
            "manufacturing"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2610-39140",
      "arxiv_id": "2610.39140",
      "title": "Numerical schemes for 1D capacitively coupled plasma fluid simulation",
      "summary_ko": "Capacitively coupled plasmas (CCP) are widely used in semiconductor manufacturing, where fluid models provide an efficient approach to the simulation of the low-temperature discharge. The model considered in this work couples the electron and the ion continuity equations, the electron energy equation and the Poisson's equation for the electric potential. We investigate the numerical schemes for the simulation of the 1D-CCP. In particular, a semi-implicit Scharfetter-Gummel (SG) scheme is adopted for the Poisson's equation, while the remaining equations are solved using the implicit SG schemes. The proposed strategy can be naturally extended to the 2D cases. Several alternative schemes are also discussed and tested. Numerical experiments compare these methods in terms of the admissible time step $Δt$, total simulation time, accuracy against a reference solution and robustness of the scheme. We propose a numerical scheme for the 1D-CCP and, to the best of our knowledge, provide the first detailed numerical analysis of such a scheme for CCP.",
      "first_seen_at": "2026-10-30T08:45:56Z",
      "discovery_sources": [],
      "authors": [
        "Yipei Chen",
        "Ganghua Fan",
        "Yuwei Fan",
        "Haoran Guan",
        "Kangxin Liu",
        "Zhonghua Qiao",
        "Yufeng Wei",
        "Peiting You",
        "Zhenyu Zou"
      ],
      "published_at": "2026-10-29T01:32:21Z",
      "updated_at": "2026-10-29T01:32:21Z",
      "abstract": "Capacitively coupled plasmas (CCP) are widely used in semiconductor manufacturing, where fluid models provide an efficient approach to the simulation of the low-temperature discharge. The model considered in this work couples the electron and the ion continuity equations, the electron energy equation and the Poisson's equation for the electric potential. We investigate the numerical schemes for the simulation of the 1D-CCP. In particular, a semi-implicit Scharfetter-Gummel (SG) scheme is adopted for the Poisson's equation, while the remaining equations are solved using the implicit SG schemes. The proposed strategy can be naturally extended to the 2D cases. Several alternative schemes are also discussed and tested. Numerical experiments compare these methods in terms of the admissible time step $Δt$, total simulation time, accuracy against a reference solution and robustness of the scheme. We propose a numerical scheme for the 1D-CCP and, to the best of our knowledge, provide the first detailed numerical analysis of such a scheme for CCP.",
      "source_url": "https://arxiv.org/abs/2610.39140",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-02T09:39:37Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "Energy systems"
          ]
        },
        "technique_matches": {
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-34184",
      "arxiv_id": "2610.34184",
      "title": "Evaluating Wi-Fi HaLow for Next-Generation Capsule Endoscopy",
      "summary_ko": "Capsule endoscopy is limited by its wireless link: insufficient throughput restricts image resolution and frame rate, whereas high-rate radios typically use higher carrier frequencies that experience greater attenuation through tissue. This work evaluates Wi-Fi HaLow (IEEE 802.11ah), a sub-GHz Wi-Fi standard, as an image link for capsule endoscopy. Across all tested combinations of modulation and coding scheme and bandwidth, the saturation TCP throughput reached 15.55 Mbps, and the fastest configuration also achieved the lowest energy per bit. Through power optimization strategies, the combined power consumption of the HaLow module and microcontroller at 1 frame per second (FPS) was reduced from 101.70 to 14.00 mW, which is comparable to that of Bluetooth Low Energy systems. In a simulated in vivo setting, the default rate control selected the highest tested MCS and bandwidth, indicating sufficient link quality; this configuration also had the lowest measured power in both the 1 FPS mode (13.59 mW) and the 30 FPS mode (174.21 mW). The capsule-to-receiver latency was 20.68 ms for a 24 KB frame, while the receiver-to-capsule latency was 4.77 ms for a command; this low-latency bidirectional communication supports real-time detection and manipulation for next-generation capsule endoscopy.",
      "first_seen_at": "2026-10-27T02:19:57Z",
      "discovery_sources": [],
      "authors": [
        "Ziyao Zhou",
        "Chen Shen",
        "Tiancheng Cao",
        "Hen-Wei Huang"
      ],
      "published_at": "2026-10-25T09:37:15Z",
      "updated_at": "2026-10-25T09:37:15Z",
      "abstract": "Capsule endoscopy is limited by its wireless link: insufficient throughput restricts image resolution and frame rate, whereas high-rate radios typically use higher carrier frequencies that experience greater attenuation through tissue. This work evaluates Wi-Fi HaLow (IEEE 802.11ah), a sub-GHz Wi-Fi standard, as an image link for capsule endoscopy. Across all tested combinations of modulation and coding scheme and bandwidth, the saturation TCP throughput reached 15.55 Mbps, and the fastest configuration also achieved the lowest energy per bit. Through power optimization strategies, the combined power consumption of the HaLow module and microcontroller at 1 frame per second (FPS) was reduced from 101.70 to 14.00 mW, which is comparable to that of Bluetooth Low Energy systems. In a simulated in vivo setting, the default rate control selected the highest tested MCS and bandwidth, indicating sufficient link quality; this configuration also had the lowest measured power in both the 1 FPS mode (13.59 mW) and the 30 FPS mode (174.21 mW). The capsule-to-receiver latency was 20.68 ms for a 24 KB frame, while the receiver-to-capsule latency was 4.77 ms for a command; this low-latency bidirectional communication supports real-time detection and manipulation for next-generation capsule endoscopy.",
      "source_url": "https://arxiv.org/abs/2610.34184",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "제조"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-02T09:39:37Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "제조": [
            "manufacturing"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2610-38201",
      "arxiv_id": "2610.38201",
      "title": "From Natural Language to Real Schematics: An Automated Workflow for Power Electronics Schematic Synthesis",
      "summary_ko": "Designing power electronics converters traditionally requires intensive human expertise across topology sizing, commercial component selection, circuit simulation verification, and schematic drafting. While Large Language Models (LLMs) show promise in electronic design automation (EDA), applying standalone LLMs directly to board-level power electronics schematic synthesis suffers from severe engineering hallucinations, including inaccurate analytical sizing, fabricated or mismatched component part numbers, violated voltage derating margins, and broken topological connectivity. In this paper, we propose a Bounded Dual-Loop Automated Schematic Synthesis Framework that translates natural language specifications into verified, editable KiCad schematics and manufacturing deliverables. Our workflow integrates six stages across three functional columns: (1) requirement parsing and knowledge-driven power stage synthesis covering non-isolated (Buck, Boost) and isolated/resonant (Flyback, DAB, half-bridge LLC) topologies; (2) contract- and evidence-coordinated component selection grounded in distributor APIs and OEM datasheets, coupled with closed-loop ngspice transient verification and stress feedback; and (3) modular control hardware integration and multi-format deliverable generation featuring native KiCad netlist round-trip verification. End-to-end case studies on a 24-36V to 12V,2A Buck converter (29 positions, 83 verified terminals) and an isolated 48V to 24V, 0.5A, Dual-Active Bridge (DAB) converter (87 positions, 341 verified terminals), alongside comparative evaluations against a Vanilla LLM baseline, demonstrate that our framework improves component verification coverage from 22.2 to 98.6, nominal value consistency from 0 to 97.7, and SPICE simulation pass rates from 0 to 100.",
      "first_seen_at": "2026-10-30T08:45:56Z",
      "discovery_sources": [],
      "authors": [
        "Bowen Su",
        "Huan Chen",
        "Jiahua Ying",
        "Ran Ou",
        "Weilong Chen",
        "Li Xue",
        "Kai Sun"
      ],
      "published_at": "2026-10-28T02:52:01Z",
      "updated_at": "2026-10-28T02:52:01Z",
      "abstract": "Designing power electronics converters traditionally requires intensive human expertise across topology sizing, commercial component selection, circuit simulation verification, and schematic drafting. While Large Language Models (LLMs) show promise in electronic design automation (EDA), applying standalone LLMs directly to board-level power electronics schematic synthesis suffers from severe engineering hallucinations, including inaccurate analytical sizing, fabricated or mismatched component part numbers, violated voltage derating margins, and broken topological connectivity. In this paper, we propose a Bounded Dual-Loop Automated Schematic Synthesis Framework that translates natural language specifications into verified, editable KiCad schematics and manufacturing deliverables. Our workflow integrates six stages across three functional columns: (1) requirement parsing and knowledge-driven power stage synthesis covering non-isolated (Buck, Boost) and isolated/resonant (Flyback, DAB, half-bridge LLC) topologies; (2) contract- and evidence-coordinated component selection grounded in distributor APIs and OEM datasheets, coupled with closed-loop ngspice transient verification and stress feedback; and (3) modular control hardware integration and multi-format deliverable generation featuring native KiCad netlist round-trip verification. End-to-end case studies on a 24-36V to 12V,2A Buck converter (29 positions, 83 verified terminals) and an isolated 48V to 24V, 0.5A, Dual-Active Bridge (DAB) converter (87 positions, 341 verified terminals), alongside comparative evaluations against a Vanilla LLM baseline, demonstrate that our framework improves component verification coverage from 22.2 to 98.6, nominal value consistency from 0 to 97.7, and SPICE simulation pass rates from 0 to 100.",
      "source_url": "https://arxiv.org/abs/2610.38201",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "시뮬레이션",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-02T09:39:37Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "Power System",
            "power systems"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ],
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-33590",
      "arxiv_id": "2610.33590",
      "title": "Nodal Inertia Pricing for RoCoF Security with Integration of Synchronous and Virtual Resources",
      "summary_ko": "Frequency regulation is becoming a more significant challenge in the undergoing low-carbon transition of modern power systems, which is heavily rely on system inertia provision. In the context of market-based energy allocation, reasonable valuation of the system inertia is crucial to its procurement, namely a worthy price should be determined for inertia providers. However, current research on the value of inertia remains insufficient, particularly during the initial phase immediately following a contingency, a critical period when the system primarily relies on the inherent inertia from conventional units and pre-engaged virtual inertia resources. Furthermore, it is found in existing studies that the Center of Inertia (COI) models exhibit significant deviations from actual physical conditions. For market-oriented reforms that explore individual differences, COI models also fail to capture the heterogeneous contributions of inertia at different nodes to system security. Given these circumstances, this paper proposes a nodal inertia pricing mechanism via a day-ahead unit commitment and inertia co-optimization model with nodal resolution. Following the proposed pricing scheme, the day- or hour-ahead inertia provision is monetized and promoted, providing regional guidance to units and devices with virtual inertia provision, which is further verified by simulation results of the South East Australia Power System.",
      "first_seen_at": "2026-10-27T02:19:57Z",
      "discovery_sources": [],
      "authors": [
        "Bomiao Liang",
        "Yu Yang",
        "Licheng Wang",
        "Lei Wan",
        "Dipti Srinivasan",
        "Fushuan Wen",
        "Youbing Zhang",
        "Beiping Hou"
      ],
      "published_at": "2026-10-24T10:32:07Z",
      "updated_at": "2026-10-24T10:32:07Z",
      "abstract": "Frequency regulation is becoming a more significant challenge in the undergoing low-carbon transition of modern power systems, which is heavily rely on system inertia provision. In the context of market-based energy allocation, reasonable valuation of the system inertia is crucial to its procurement, namely a worthy price should be determined for inertia providers. However, current research on the value of inertia remains insufficient, particularly during the initial phase immediately following a contingency, a critical period when the system primarily relies on the inherent inertia from conventional units and pre-engaged virtual inertia resources. Furthermore, it is found in existing studies that the Center of Inertia (COI) models exhibit significant deviations from actual physical conditions. For market-oriented reforms that explore individual differences, COI models also fail to capture the heterogeneous contributions of inertia at different nodes to system security. Given these circumstances, this paper proposes a nodal inertia pricing mechanism via a day-ahead unit commitment and inertia co-optimization model with nodal resolution. Following the proposed pricing scheme, the day- or hour-ahead inertia provision is monetized and promoted, providing regional guidance to units and devices with virtual inertia provision, which is further verified by simulation results of the South East Australia Power System.",
      "source_url": "https://arxiv.org/abs/2610.33590",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "시뮬레이션",
        "디지털트윈"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-02T09:39:37Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "energy systems"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ],
          "디지털트윈": [
            "digital-twin"
          ]
        }
      },
      "id": "auto-arxiv-2610-33204",
      "arxiv_id": "2610.33204",
      "title": "Flow Mesh: A Velocity-Regularized Flow Matching Framework for Stable Distributional Modeling of Physical Fields on Unstructured Meshes",
      "summary_ko": "Thermal-fluid field distributions underpin the analysis and operation of energy infrastructure, from nuclear-reactor cooling channels to industrial heat exchangers, but resolving these distributions through computational fluid dynamics requires prohibitively large simulation ensembles. Their complex geometries also demand unstructured meshes that are poorly accommodated by generative models developed for regular grids. Here we introduce FlowMesh, a generative framework that combines permutation-invariant mesh encoding with deterministic latent flow matching and an l1 penalty on the learned transport velocity. FlowMesh generates full-field realizations directly on fixed unstructured meshes without auxiliary grid interpolation or a stochastic denoising chain. Across lid-driven-cavity, pressurized-water-reactor subchannel and heat-exchanger datasets, evaluated over six matched training seeds, FlowMesh achieves macro-averaged probability-density-function coverage of 91.7%-97.0%, exceeding an architecture-controlled latent diffusion model by $11.6$--$45.4$ percentage points. Latent-velocity regularization increases coverage by $3.8$ percentage points for reactor flows and 1.1 points for heat-exchanger flows, while reducing temperature-field Kullback--Leibler divergence by 1.7-2.4-fold. Each full-field realization requires 1.23 \\pm 0.15 s, providing a 585--1902-fold wall-clock acceleration over computational fluid dynamics. FlowMesh therefore makes large-scale generative characterization of thermal-fluid states computationally feasible, providing a foundation for data-driven analysis and digital-twin development in fixed-geometry energy systems.",
      "first_seen_at": "2026-10-27T02:19:57Z",
      "discovery_sources": [],
      "authors": [
        "Siddharth Verma",
        "Jaewan Park",
        "Jay Phil Yoo",
        "Kazuma Kobayashi",
        "Souvik Chakraborty",
        "Syed Bahauddin Alam"
      ],
      "published_at": "2026-10-23T18:49:23Z",
      "updated_at": "2026-10-23T18:49:23Z",
      "abstract": "Thermal-fluid field distributions underpin the analysis and operation of energy infrastructure, from nuclear-reactor cooling channels to industrial heat exchangers, but resolving these distributions through computational fluid dynamics requires prohibitively large simulation ensembles. Their complex geometries also demand unstructured meshes that are poorly accommodated by generative models developed for regular grids. Here we introduce FlowMesh, a generative framework that combines permutation-invariant mesh encoding with deterministic latent flow matching and an l1 penalty on the learned transport velocity. FlowMesh generates full-field realizations directly on fixed unstructured meshes without auxiliary grid interpolation or a stochastic denoising chain. Across lid-driven-cavity, pressurized-water-reactor subchannel and heat-exchanger datasets, evaluated over six matched training seeds, FlowMesh achieves macro-averaged probability-density-function coverage of 91.7%-97.0%, exceeding an architecture-controlled latent diffusion model by $11.6$--$45.4$ percentage points. Latent-velocity regularization increases coverage by $3.8$ percentage points for reactor flows and 1.1 points for heat-exchanger flows, while reducing temperature-field Kullback--Leibler divergence by 1.7-2.4-fold. Each full-field realization requires 1.23 \\pm 0.15 s, providing a 585--1902-fold wall-clock acceleration over computational fluid dynamics. FlowMesh therefore makes large-scale generative characterization of thermal-fluid states computationally feasible, providing a foundation for data-driven analysis and digital-twin development in fixed-geometry energy systems.",
      "source_url": "https://arxiv.org/abs/2610.33204",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "데이터 분석"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-03T09:42:47Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power systems"
          ]
        },
        "technique_matches": {
          "데이터 분석": [
            "Time Series"
          ]
        }
      },
      "id": "auto-arxiv-2609-39257",
      "arxiv_id": "2609.39257",
      "title": "From Benchmarks to Production: Transferring Time Series Anomaly Detection Methods for Electricity Production Monitoring",
      "summary_ko": "Accurate forecasting of electricity production is essential for maintaining the operational efficiency and strategic planning of energy utilities. In industrial settings, such forecasts are generated daily to ensure supply-demand balance and optimal management of production assets. However, the increasing complexity of modern power systems and data flows poses significant challenges for ensuring the reliability and consistency of these forecasts. This paper addresses the problem of anomaly detection in short-term production forecasts at EDF, formulated as identifying atypical intra-day patterns that may signal data quality issues or operational irregularities. We introduce TAMIS, a scalable and interpretable system that analyzes daily production time series to automatically detect anomalous days based on deviations from historical patterns learned from past data. Designed for human-in-the-loop workflows, TAMIS surfaces top-ranked anomalies through an automated daily newsletter, enabling efficient expert review and continuous monitoring. An extensive experimental evaluation on real-world industrial data demonstrates that TAMIS achieves the best accuracy-efficiency trade-off compared to baseline methods. To foster further research and reproducibility, we publicly release the anonymized application datasets used in our study.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Nicolas Vautier",
        "Paul Caron",
        "Nardi Xhepi",
        "Félicie Bizeul",
        "Manel Boumghar",
        "Christophe Degouy",
        "Paul Boniol"
      ],
      "published_at": "2026-09-30T08:20:20Z",
      "updated_at": "2026-09-30T08:20:20Z",
      "abstract": "Accurate forecasting of electricity production is essential for maintaining the operational efficiency and strategic planning of energy utilities. In industrial settings, such forecasts are generated daily to ensure supply-demand balance and optimal management of production assets. However, the increasing complexity of modern power systems and data flows poses significant challenges for ensuring the reliability and consistency of these forecasts. This paper addresses the problem of anomaly detection in short-term production forecasts at EDF, formulated as identifying atypical intra-day patterns that may signal data quality issues or operational irregularities. We introduce TAMIS, a scalable and interpretable system that analyzes daily production time series to automatically detect anomalous days based on deviations from historical patterns learned from past data. Designed for human-in-the-loop workflows, TAMIS surfaces top-ranked anomalies through an automated daily newsletter, enabling efficient expert review and continuous monitoring. An extensive experimental evaluation on real-world industrial data demonstrates that TAMIS achieves the best accuracy-efficiency trade-off compared to baseline methods. To foster further research and reproducibility, we publicly release the anonymized application datasets used in our study.",
      "source_url": "https://arxiv.org/abs/2609.39257",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "물류",
        "철도"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-03T09:42:47Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "물류": [
            "freight"
          ],
          "철도": [
            "railway",
            "Railroad"
          ]
        },
        "technique_matches": {
          "최적화": [
            "mixed-integer"
          ]
        }
      },
      "id": "auto-arxiv-2610-41403",
      "arxiv_id": "2610.41403",
      "title": "A Dual-Guided Column-Generation-Based Matheuristic for Integrated Railroad Blocking",
      "summary_ko": "Integrated railroad blocking is a central planning problem in freight railway operations, which requires coordinated decisions on block design, blocking sequences, and block routing. We propose a dual-guided column-generation-based matheuristic (DG-CGM) that uses linear programming (LP) dual information to identify promising transportation paths and incorporates them into restricted mixed-integer programs to improve feasible solutions. Starting from an initial plan built by construction, DG-CGM alternates destroy and local repair with path generation, which uses dual prices from restricted LPs of a Dantzig-Wolfe reformulation to identify promising transportation paths. When local improvements continue to stagnate, we further apply joint repair to combine alternatives across demand groups. To address the coupling between logical and physical path pricing, we introduce a joint reduced cost that eliminates path-specific dual variables from pricing, enabling more efficient column identification through exact labeling algorithms. We evaluate the performance of DG-CGM on both generated instances and real-world cases. On 150 generated instances with 50-150 nodes, DG-CGM achieves the optimal cost on 89 of the 91 instances solved to proven optimality by Gurobi, with a geometric mean runtime equal to 5.2% of that for Gurobi and a mean relative cost reduction of 3.4%. On six full-scale scenarios derived from the 2026 INFORMS Railway Applications Section competition data, each with 47,193 nodes, DG-CGM obtains feasible solutions within two hours that serve 95.9%-99.6% of the requested freight volume, providing practical blocking plans for large-scale railway operations. The code, instances, and computational results are publicly available at https://github.com/Zhengzhong-You/IRBP.",
      "first_seen_at": "2026-11-02T09:39:37Z",
      "discovery_sources": [],
      "authors": [
        "Yida Xu",
        "Zhengzhong Ricky You",
        "Baichuan Mo"
      ],
      "published_at": "2026-10-30T08:14:30Z",
      "updated_at": "2026-10-30T08:14:30Z",
      "abstract": "Integrated railroad blocking is a central planning problem in freight railway operations, which requires coordinated decisions on block design, blocking sequences, and block routing. We propose a dual-guided column-generation-based matheuristic (DG-CGM) that uses linear programming (LP) dual information to identify promising transportation paths and incorporates them into restricted mixed-integer programs to improve feasible solutions. Starting from an initial plan built by construction, DG-CGM alternates destroy and local repair with path generation, which uses dual prices from restricted LPs of a Dantzig-Wolfe reformulation to identify promising transportation paths. When local improvements continue to stagnate, we further apply joint repair to combine alternatives across demand groups. To address the coupling between logical and physical path pricing, we introduce a joint reduced cost that eliminates path-specific dual variables from pricing, enabling more efficient column identification through exact labeling algorithms. We evaluate the performance of DG-CGM on both generated instances and real-world cases. On 150 generated instances with 50-150 nodes, DG-CGM achieves the optimal cost on 89 of the 91 instances solved to proven optimality by Gurobi, with a geometric mean runtime equal to 5.2% of that for Gurobi and a mean relative cost reduction of 3.4%. On six full-scale scenarios derived from the 2026 INFORMS Railway Applications Section competition data, each with 47,193 nodes, DG-CGM obtains feasible solutions within two hours that serve 95.9%-99.6% of the requested freight volume, providing practical blocking plans for large-scale railway operations. The code, instances, and computational results are publicly available at https://github.com/Zhengzhong-You/IRBP.",
      "source_url": "https://arxiv.org/abs/2610.41403",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "AI",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-03T09:42:47Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power systems"
          ]
        },
        "technique_matches": {
          "AI": [
            "AI",
            "artificial intelligence"
          ],
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-36441",
      "arxiv_id": "2610.36441",
      "title": "Phase-Coordinated Model Predictive Control for AI Training Power Smoothing",
      "summary_ko": "Periodic power fluctuations from artificial intelligence (AI) training workloads can excite oscillations in power systems. This paper proposes a model predictive control (MPC) framework that exploits short start-time delays to mitigate power fluctuations at the computing source. By coordinating the relative phases of independent training jobs, the proposed method reduces the superposition of oscillatory power components without interrupting running jobs or requiring additional power-smoothing hardware. To obtain a tractable optimization formulation, cosine and sine projections are employed to quantify the dominant oscillatory power components, and their magnitudes are used to construct a convex quadratic power-smoothing objective. The receding-horizon framework updates launch decisions as new workload information becomes available while maintaining scheduling feasibility under limited computation time. Numerical results demonstrate reductions of 74.39\\% in mean-square power fluctuations within the 0.2--2-Hz band and 46.6\\% in power standard deviation, with an average launch delay of 2.188~s. All launch deadlines are satisfied, and the maximum end-to-end computation time is 0.390~s, below the 0.50-s MPC update period.",
      "first_seen_at": "2026-10-29T08:39:16Z",
      "discovery_sources": [],
      "authors": [
        "Jie Zhu",
        "Ye Tian",
        "Yinliang Xu"
      ],
      "published_at": "2026-10-27T00:44:34Z",
      "updated_at": "2026-10-27T00:44:34Z",
      "abstract": "Periodic power fluctuations from artificial intelligence (AI) training workloads can excite oscillations in power systems. This paper proposes a model predictive control (MPC) framework that exploits short start-time delays to mitigate power fluctuations at the computing source. By coordinating the relative phases of independent training jobs, the proposed method reduces the superposition of oscillatory power components without interrupting running jobs or requiring additional power-smoothing hardware. To obtain a tractable optimization formulation, cosine and sine projections are employed to quantify the dominant oscillatory power components, and their magnitudes are used to construct a convex quadratic power-smoothing objective. The receding-horizon framework updates launch decisions as new workload information becomes available while maintaining scheduling feasibility under limited computation time. Numerical results demonstrate reductions of 74.39\\% in mean-square power fluctuations within the 0.2--2-Hz band and 46.6\\% in power standard deviation, with an average launch delay of 2.188~s. All launch deadlines are satisfied, and the maximum end-to-end computation time is 0.390~s, below the 0.50-s MPC update period.",
      "source_url": "https://arxiv.org/abs/2610.36441",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-03T09:42:47Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power system",
            "Power Systems",
            "power grid"
          ]
        },
        "technique_matches": {
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-36357",
      "arxiv_id": "2610.36357",
      "title": "Multi-time-scale Control of Power Systems",
      "summary_ko": "The power grid has been dominated by large synchronous generators and clearly separated control layers. Today, power-electronics energy conversion interfaces are rapidly being deployed calling for another look at system-level control hierarchies. This article examines how multi-time-scale modeling, feedback control, and feedback optimization can help reinterpret the traditional organically grown hierarchy of primary control, automatic generation control, and economic dispatch, and enable new approaches for control of contemporary power systems. Beginning with an overview of power system models and conventional operational practices, the article develops connections to modern control architectures that can coordinate heterogeneous resources, account for operational constraints, and exploit faster actuation without overlooking first-principles physics.",
      "first_seen_at": "2026-10-29T08:39:16Z",
      "discovery_sources": [],
      "authors": [
        "Emiliano Dall'Anese",
        "John W. Simpson-Porco",
        "Florian Dorfler",
        "Saverio Bolognani",
        "Sairaj Dhople"
      ],
      "published_at": "2026-10-26T22:20:46Z",
      "updated_at": "2026-10-26T22:20:46Z",
      "abstract": "The power grid has been dominated by large synchronous generators and clearly separated control layers. Today, power-electronics energy conversion interfaces are rapidly being deployed calling for another look at system-level control hierarchies. This article examines how multi-time-scale modeling, feedback control, and feedback optimization can help reinterpret the traditional organically grown hierarchy of primary control, automatic generation control, and economic dispatch, and enable new approaches for control of contemporary power systems. Beginning with an overview of power system models and conventional operational practices, the article develops connections to modern control architectures that can coordinate heterogeneous resources, account for operational constraints, and exploit faster actuation without overlooking first-principles physics.",
      "source_url": "https://arxiv.org/abs/2610.36357",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-03T09:42:47Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power system"
          ]
        },
        "technique_matches": {
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-35815",
      "arxiv_id": "2610.35815",
      "title": "Security Region Compression via Hybrid Vertex Pruning for Enhanced Computational Efficiency",
      "summary_ko": "The security region (SR) specifies a joint feasible region in the operational space involving various installations within the power system. It enables dynamic allocation of capacity among these energy units, thereby unlocking additional grid potential for power delivery and mitigating congestion. By constructing a convex hull from a set of vertices to form a polytope, the SR can be effectively represented with an acceptable accuracy loss. However, the SR computation inevitably generates many low-quality vertices, which contribute little to the overall hypervolume and can be discarded. By introducing SR compression, the SR data size can be reduced, which in turn facilitates the memory-efficient computation of SR and time-efficient solution of SR-related optimization problems. Accordingly, this paper proposes a vertex pruning approach that mixes local and backward search policies to achieve effective SR compression. In particular, the backward search strategy allows previously pruned vertices to be recycled when they are re-identified as critical. Subsequently, we incorporate this approach to enhance computational efficiency in the aforementioned applications. Numerical validations are conducted on two realistic medium-voltage Dutch grid cases. The results demonstrate at most 1.65% hypervolume loss with over 96.0\\% SR data size reduction after applying the proposed compression approach. Empowered by this compression, the 8-dimensional (8D) SR becomes computationally tractable. Moreover, in relevant grid-aware computation challenges, the solving time can be reduced by at least 52.9% with at most an 1.36% optimality loss. All above validate both the efficacy and values of the proposed SR compression approach.",
      "first_seen_at": "2026-10-29T08:39:16Z",
      "discovery_sources": [],
      "authors": [
        "Sicheng Gong",
        "Zeyu Liu",
        "Danyang Xu",
        "Hongjie Jia"
      ],
      "published_at": "2026-10-26T17:43:40Z",
      "updated_at": "2026-10-26T17:43:40Z",
      "abstract": "The security region (SR) specifies a joint feasible region in the operational space involving various installations within the power system. It enables dynamic allocation of capacity among these energy units, thereby unlocking additional grid potential for power delivery and mitigating congestion. By constructing a convex hull from a set of vertices to form a polytope, the SR can be effectively represented with an acceptable accuracy loss. However, the SR computation inevitably generates many low-quality vertices, which contribute little to the overall hypervolume and can be discarded. By introducing SR compression, the SR data size can be reduced, which in turn facilitates the memory-efficient computation of SR and time-efficient solution of SR-related optimization problems. Accordingly, this paper proposes a vertex pruning approach that mixes local and backward search policies to achieve effective SR compression. In particular, the backward search strategy allows previously pruned vertices to be recycled when they are re-identified as critical. Subsequently, we incorporate this approach to enhance computational efficiency in the aforementioned applications. Numerical validations are conducted on two realistic medium-voltage Dutch grid cases. The results demonstrate at most 1.65% hypervolume loss with over 96.0\\% SR data size reduction after applying the proposed compression approach. Empowered by this compression, the 8-dimensional (8D) SR becomes computationally tractable. Moreover, in relevant grid-aware computation challenges, the solving time can be reduced by at least 52.9% with at most an 1.36% optimality loss. All above validate both the efficacy and values of the proposed SR compression approach.",
      "source_url": "https://arxiv.org/abs/2610.35815",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "AI",
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-03T09:42:47Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power systems"
          ]
        },
        "technique_matches": {
          "AI": [
            "neural network"
          ],
          "시뮬레이션": [
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2610-35640",
      "arxiv_id": "2610.35640",
      "title": "FedAvgIm: Federated Learning for Multi-Site Impedance Identification of Inverter-Based Resources",
      "summary_ko": "With the increasing integration of inverter-based resources (IBRs) into power systems, assessing small-signal interaction stability has become essential. Impedance/admittance models enable frequency-domain characterization of IBR dynamics without requiring access to proprietary internal control details, but measurement-based identification is time-consuming and must be repeated across operating points. This paper proposes FedAvgIm, a privacy-aware federated-learning framework for multi-site dq-admittance identification under severe local data scarcity and raw-data-locality constraints. Each IBR site trains a local feed-forward neural network using a small set of operating-point-frequency samples, while only model parameters are shared with a cloud aggregator that performs sample-size-weighted FedAvg to update a global model and broadcast it back to all sites. To demonstrate effectiveness, EMT measurement-based admittance datasets are constructed for nine grid-following IBRs with different parameters, architectures, measurement-noise levels, and non-IID settings, where each client has only 40-390 training samples. Results show that FedAvgIm achieves performance close to centralized training and consistently outperforms local-only learning and raw-data-locality-preserving transfer-learning baselines. Additional studies show that FedAvgIm maintains stable learning behavior under measurement noise and remains beneficial under moderate non-IID conditions. The estimated dq-admittance models are further applied to generalized Nyquist stability analysis and validated against EMT time-domain simulations under multiple operating points and grid-strength conditions.",
      "doi": "10.1109/tsg.2026.3717637",
      "first_seen_at": "2026-10-29T08:39:16Z",
      "discovery_sources": [],
      "authors": [
        "Quang Manh Hoang",
        "Guilherme Vieira Hollweg",
        "Akhtar Hussain",
        "Wencong Su",
        "Van-Hai Bui"
      ],
      "published_at": "2026-10-26T15:50:11Z",
      "updated_at": "2026-10-26T15:50:11Z",
      "abstract": "With the increasing integration of inverter-based resources (IBRs) into power systems, assessing small-signal interaction stability has become essential. Impedance/admittance models enable frequency-domain characterization of IBR dynamics without requiring access to proprietary internal control details, but measurement-based identification is time-consuming and must be repeated across operating points. This paper proposes FedAvgIm, a privacy-aware federated-learning framework for multi-site dq-admittance identification under severe local data scarcity and raw-data-locality constraints. Each IBR site trains a local feed-forward neural network using a small set of operating-point-frequency samples, while only model parameters are shared with a cloud aggregator that performs sample-size-weighted FedAvg to update a global model and broadcast it back to all sites. To demonstrate effectiveness, EMT measurement-based admittance datasets are constructed for nine grid-following IBRs with different parameters, architectures, measurement-noise levels, and non-IID settings, where each client has only 40-390 training samples. Results show that FedAvgIm achieves performance close to centralized training and consistently outperforms local-only learning and raw-data-locality-preserving transfer-learning baselines. Additional studies show that FedAvgIm maintains stable learning behavior under measurement noise and remains beneficial under moderate non-IID conditions. The estimated dq-admittance models are further applied to generalized Nyquist stability analysis and validated against EMT time-domain simulations under multiple operating points and grid-strength conditions.",
      "source_url": "https://arxiv.org/abs/2610.35640",
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "제조"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-04T09:29:09Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "제조": [
            "manufacturing"
          ]
        },
        "technique_matches": {
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-25382",
      "arxiv_id": "2610.25382",
      "title": "Design and synthesis of laser inscribed flexible Bragg gratings containing silicon oxynitride-doped silicon thin film for authentication applications with the insights from X-Ray Absorption Spectroscopy and Quantum Mechanical Calculations",
      "summary_ko": "Silicon-on-insulator is mostly the main driving material system for the field of silicon photonics, including PICs, due to high index contrast and compatibility with current semiconductor manufacturing processes [1]. A recent example is silicon oxynitride-doped silicon [2] discovered recently by our group, offering to set a new position for the fields of silicon photonics with a precise control of refractive index from 2.1 to 2.5 together with the photosensitivity property at different wavelengths. In this study, we demonstrate that the synthesis of silicon oxynitride-doped silicon can be performed on a flexible substrate for the fabrication of Bragg gratings (BG) at low temperatures below 100 degrees Celsius that are emerging for advanced applications. Their use in preserving ultra-fast lasers inscribed micro-information has been demonstrated numerically and experimentally due to its multispectral reflection behaviour from the ultraviolet (UV) to the near-infrared (NIR) regions. The unique structural characteristics of silicon oxynitride-doped silicon were studied at the Si L, O K and N K edges using soft X-ray absorption studies. The quantum mechanical calculations provide the potential formation mechanism of SiON-doped Si, whereas the crystal structure prediction for bulk Si3N4-xOx was conducted using the particle swarm optimization method in combination with density functional theory (DFT).",
      "first_seen_at": "2026-10-20T07:44:48Z",
      "discovery_sources": [],
      "authors": [
        "Ali Karatutlu",
        "Timuçin Emre Tabaru",
        "Umut Taylan",
        "Zehra Gizem Mutlay",
        "Doğukan Hazar Özbey",
        "Okan Uslu",
        "Hamid-Reza Bahari",
        "Esra Kendir Tekgül",
        "Andriy Budnyk",
        "İremnur Duru",
        "Mustafa Fatih Genisel",
        "Zeynep Reyhan Öztürk",
        "Taylan Gorkan",
        "Engin Durgun",
        "Bülend Ortaç"
      ],
      "published_at": "2026-10-18T16:31:01Z",
      "updated_at": "2026-10-18T16:31:01Z",
      "abstract": "Silicon-on-insulator is mostly the main driving material system for the field of silicon photonics, including PICs, due to high index contrast and compatibility with current semiconductor manufacturing processes [1]. A recent example is silicon oxynitride-doped silicon [2] discovered recently by our group, offering to set a new position for the fields of silicon photonics with a precise control of refractive index from 2.1 to 2.5 together with the photosensitivity property at different wavelengths. In this study, we demonstrate that the synthesis of silicon oxynitride-doped silicon can be performed on a flexible substrate for the fabrication of Bragg gratings (BG) at low temperatures below 100 degrees Celsius that are emerging for advanced applications. Their use in preserving ultra-fast lasers inscribed micro-information has been demonstrated numerically and experimentally due to its multispectral reflection behaviour from the ultraviolet (UV) to the near-infrared (NIR) regions. The unique structural characteristics of silicon oxynitride-doped silicon were studied at the Si L, O K and N K edges using soft X-ray absorption studies. The quantum mechanical calculations provide the potential formation mechanism of SiON-doped Si, whereas the crystal structure prediction for bulk Si3N4-xOx was conducted using the particle swarm optimization method in combination with density functional theory (DFT).",
      "source_url": "https://arxiv.org/abs/2610.25382",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-04T09:29:09Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power systems"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2610-39819",
      "arxiv_id": "2610.39819",
      "title": "Modeling and Parametrization of Large-scale Hydrogen Electrolyzers for Transient and Frequency Stability Analysis",
      "summary_ko": "Hydrogen Electrolyzers (HEs), particularly Proton Exchange Membrane (PEM)-based systems, are flexible loads capable of providing grid ancillary services and enhancing the stability of renewable-dominated power systems due to their fast electrical response. However, the literature often lacks clear guidance on the structure, modeling, and parametrization of large-scale HEs for transient and frequency stability analyses. This paper presents a detailed configuration for utility-scale HE plants, using the stack as the fundamental unit, supported by a realistic parametrization based on commercial components. Furthermore, this work extends the Generalized ESS Model (GEM), a linearized state-space model previously utilized with other converter-interfaced energy storage systems, to PEM HEs for the first time. To facilitate plant scalability, a robust method for scaling GEM matrices across different capacities is proposed. Finally, a measurement-based methodology is developed to estimate the GEM parameters and subsequently extract the equivalent electrical circuit parameters. Comprehensive simulations on the WSCC system under various disturbances validate the accuracy of the derived GEM, the effectiveness of the scaling approach, and the robustness of the parameter identification methodology.",
      "first_seen_at": "2026-10-30T08:45:56Z",
      "discovery_sources": [],
      "authors": [
        "Mahmoud K. Abdelhamid",
        "Lukas Sigrist",
        "Álvaro Ortega"
      ],
      "published_at": "2026-10-29T11:02:39Z",
      "updated_at": "2026-10-29T11:02:39Z",
      "abstract": "Hydrogen Electrolyzers (HEs), particularly Proton Exchange Membrane (PEM)-based systems, are flexible loads capable of providing grid ancillary services and enhancing the stability of renewable-dominated power systems due to their fast electrical response. However, the literature often lacks clear guidance on the structure, modeling, and parametrization of large-scale HEs for transient and frequency stability analyses. This paper presents a detailed configuration for utility-scale HE plants, using the stack as the fundamental unit, supported by a realistic parametrization based on commercial components. Furthermore, this work extends the Generalized ESS Model (GEM), a linearized state-space model previously utilized with other converter-interfaced energy storage systems, to PEM HEs for the first time. To facilitate plant scalability, a robust method for scaling GEM matrices across different capacities is proposed. Finally, a measurement-based methodology is developed to estimate the GEM parameters and subsequently extract the equivalent electrical circuit parameters. Comprehensive simulations on the WSCC system under various disturbances validate the accuracy of the derived GEM, the effectiveness of the scaling approach, and the robustness of the parameter identification methodology.",
      "source_url": "https://arxiv.org/abs/2610.39819",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "AI"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-04T09:29:09Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robot",
            "robotics",
            "Robotic"
          ]
        },
        "technique_matches": {
          "AI": [
            "reinforcement learning"
          ]
        }
      },
      "id": "auto-arxiv-2611-03622",
      "arxiv_id": "2611.03622",
      "title": "D&A: Decoupling Description and Assessment Enables VLMs as Zero-Shot Robotic Reward Models",
      "summary_ko": "Vision-Language Models (VLMs) hold promise for zero-shot video task-progress estimation in robot learning, yet existing frameworks frequently suffer from confirmation bias, hallucinating progress in failed executions due to implicit success assumptions or relative temporal ranking designs. Naive prompting of frontier models collapses into a binary success detector that fails to capture nuanced, non-linear progress and failures. While post-training approaches can mitigate this through explicit training, they demand expensive real-world robotics data and require computationally intensive post-training for every individual base model. To resolve these limitations, we introduce D&A (Describe & Assess): our approach decouples progress estimation into an objective, task-grounded textual description of scene dynamics followed by an assessment phase using explicit rubrics. By compelling the VLM to synthesize an explicit, text-based definition of absolute success before scoring, the framework anchors evaluations to an external, global criterion rather than the video's internal timeline. Empirical results demonstrate that our method calibrates zero-shot estimates and mitigates confirmation bias, providing fine-grained, partial progress metrics that accurately penalize failed executions while correctly interpolating successful runs. Being purely prompt-based, D&A generalizes across tasks, embodiments, and benchmarks---matching or surpassing post-trained reward models---and its reward estimates enable efficient downstream reinforcement learning.",
      "first_seen_at": "2026-11-03T09:42:47Z",
      "discovery_sources": [],
      "authors": [
        "Marius Memmel",
        "Shawn Yin",
        "Urvi Rutia",
        "Angad Wadhwa",
        "Eldon Li",
        "Gavin Angeloff",
        "Ankit Goyal",
        "Jesse Zhang",
        "Dieter Fox",
        "Fabio Ramos",
        "Anqi Li",
        "Abhishek Gupta"
      ],
      "published_at": "2026-11-02T18:52:11Z",
      "updated_at": "2026-11-02T18:52:11Z",
      "abstract": "Vision-Language Models (VLMs) hold promise for zero-shot video task-progress estimation in robot learning, yet existing frameworks frequently suffer from confirmation bias, hallucinating progress in failed executions due to implicit success assumptions or relative temporal ranking designs. Naive prompting of frontier models collapses into a binary success detector that fails to capture nuanced, non-linear progress and failures. While post-training approaches can mitigate this through explicit training, they demand expensive real-world robotics data and require computationally intensive post-training for every individual base model. To resolve these limitations, we introduce D&A (Describe & Assess): our approach decouples progress estimation into an objective, task-grounded textual description of scene dynamics followed by an assessment phase using explicit rubrics. By compelling the VLM to synthesize an explicit, text-based definition of absolute success before scoring, the framework anchors evaluations to an external, global criterion rather than the video's internal timeline. Empirical results demonstrate that our method calibrates zero-shot estimates and mitigates confirmation bias, providing fine-grained, partial progress metrics that accurately penalize failed executions while correctly interpolating successful runs. Being purely prompt-based, D&A generalizes across tasks, embodiments, and benchmarks---matching or surpassing post-trained reward models---and its reward estimates enable efficient downstream reinforcement learning.",
      "source_url": "https://arxiv.org/abs/2611.03622",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "우주"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-04T09:29:09Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "우주": [
            "Satellite",
            "satellites"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2611-03440",
      "arxiv_id": "2611.03440",
      "title": "Beyond GNSS: Leveraging LEO Satellite Signals of Opportunity for Doppler Positioning",
      "summary_ko": "Large constellations of low Earth orbit (LEO) satellites provide ubiquitous global connectivity; yet, their rapid motion introduces significant Doppler shift effects. While challenging for communication, these effects can be leveraged for precise user terminal (UT) positioning, particularly in GNSS-denied environments where traditional satellite-based navigation struggles. Unlike GNSS, most LEO satellites lack dedicated navigation signals, motivating signals-of-opportunity (SOPs) approaches that repurpose existing LEO signals and use publicly available two-line element (TLE) files for orbital information, although ephemeris errors can limit accuracy. This article explores the fundamental principles, challenges, and enabling technologies for LEO-based Doppler shift positioning, highlighting its advantages over conventional non-Doppler methods, and discussing future research directions. Furthermore, to facilitate realistic and reproducible performance evaluation beyond stochastic modeling, we propose SionnaNTN-RT, a ray tracing framework for non-terrestrial network (NTN) built upon Sionna, enabling end-to-end simulation of Doppler-based positioning. Simulation results demonstrate that Doppler-based positioning can achieve high positioning accuracy in both urban and open-sky environments, highlighting its promise for next-generation positioning systems.",
      "first_seen_at": "2026-11-03T09:42:47Z",
      "discovery_sources": [],
      "authors": [
        "Md. Ali Hasan",
        "M. Humayun Kabir",
        "Sangmin Han",
        "Wonjae Shin",
        "H. Vincent Poor"
      ],
      "published_at": "2026-11-02T16:54:37Z",
      "updated_at": "2026-11-02T16:54:37Z",
      "abstract": "Large constellations of low Earth orbit (LEO) satellites provide ubiquitous global connectivity; yet, their rapid motion introduces significant Doppler shift effects. While challenging for communication, these effects can be leveraged for precise user terminal (UT) positioning, particularly in GNSS-denied environments where traditional satellite-based navigation struggles. Unlike GNSS, most LEO satellites lack dedicated navigation signals, motivating signals-of-opportunity (SOPs) approaches that repurpose existing LEO signals and use publicly available two-line element (TLE) files for orbital information, although ephemeris errors can limit accuracy. This article explores the fundamental principles, challenges, and enabling technologies for LEO-based Doppler shift positioning, highlighting its advantages over conventional non-Doppler methods, and discussing future research directions. Furthermore, to facilitate realistic and reproducible performance evaluation beyond stochastic modeling, we propose SionnaNTN-RT, a ray tracing framework for non-terrestrial network (NTN) built upon Sionna, enabling end-to-end simulation of Doppler-based positioning. Simulation results demonstrate that Doppler-based positioning can achieve high positioning accuracy in both urban and open-sky environments, highlighting its promise for next-generation positioning systems.",
      "source_url": "https://arxiv.org/abs/2611.03440",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "우주",
        "물류"
      ],
      "techniques": [
        "AI"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-04T09:29:09Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "우주": [
            "Satellite"
          ],
          "물류": [
            "logistics"
          ]
        },
        "technique_matches": {
          "AI": [
            "Reinforcement Learning"
          ]
        }
      },
      "id": "auto-arxiv-2611-02809",
      "arxiv_id": "2611.02809",
      "title": "Offline Approximate Value Iteration: A Reinforcement Learning Approach for Satellite Constellation Deployment",
      "summary_ko": "This paper introduces a reinforcement learning-based approximate dynamic programming approach to optimize the deployment of Walker-type satellite constellations under uncertainties, including launch failures and satellite malfunctions. We formulate the problem as a Markov Decision Process that incorporates low-injection orbit launches, continuous low-thrust orbit raising, and variable satellite loads. We adopt the offline approximate value iteration solution method, using linear function approximation with basis functions and action pruning strategies to substantially reduce the computational and memory requirements for finding optimal decision policies. Preliminary results demonstrate convergence to policies with low expected costs and deployment times, both of which are lower than those of a rolling-horizon baseline, showcasing a novel application of approximate dynamic programming in space logistics.",
      "first_seen_at": "2026-11-03T09:42:47Z",
      "discovery_sources": [],
      "authors": [
        "Tristan Leuridan",
        "Yiping Fang",
        "Adam Abdin"
      ],
      "published_at": "2026-11-02T09:17:21Z",
      "updated_at": "2026-11-02T09:17:21Z",
      "abstract": "This paper introduces a reinforcement learning-based approximate dynamic programming approach to optimize the deployment of Walker-type satellite constellations under uncertainties, including launch failures and satellite malfunctions. We formulate the problem as a Markov Decision Process that incorporates low-injection orbit launches, continuous low-thrust orbit raising, and variable satellite loads. We adopt the offline approximate value iteration solution method, using linear function approximation with basis functions and action pruning strategies to substantially reduce the computational and memory requirements for finding optimal decision policies. Preliminary results demonstrate convergence to policies with low expected costs and deployment times, both of which are lower than those of a rolling-horizon baseline, showcasing a novel application of approximate dynamic programming in space logistics.",
      "source_url": "https://arxiv.org/abs/2611.02809",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "AI"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-04T09:29:09Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power system",
            "Power Flow"
          ]
        },
        "technique_matches": {
          "AI": [
            "AI",
            "artificial intelligence",
            "machine-learning"
          ]
        }
      },
      "id": "auto-arxiv-2610-38945",
      "arxiv_id": "2610.38945",
      "title": "A Defense-Aware Explainable AI Framework for Feature-Level Vulnerability Analysis in Optimal Power Flow",
      "summary_ko": "Optimal power flow (OPF) is a foundational analysis in power system operation, and inaccuracies in OPF decision-making can result in significant operational and security consequences. With the increasing adoption of machine-learning-based OPF surrogate models for real-time and large-scale applications, power system operation has become more susceptible to cyber threats, including data manipulation and false data injection attacks. Explainable artificial intelligence (XAI), particularly Shapley value-based approaches, has been employed to quantify the influence of input features on OPF outputs. Features exhibiting high influence are commonly interpreted as vulnerable. However, this interpretation neglects the role of attack detection mechanisms in the system, which typically monitor input-to-input relationships to detect anomalous behavior. Consequently, highly influential features may be readily detectable and may not represent the most vulnerable attack surfaces. To address this limitation, we propose a defense-aware explainable AI framework that explicitly incorporates attack detectability alongside operational impact. The framework integrates an OPF surrogate model representing input-to-output dependencies with a vanilla autoencoder representing input-to-input dependencies. Shapley-based explainability is applied to both models to construct a feature-level vulnerability metric. Empirical evaluation on IEEE test systems demonstrates that the proposed framework more accurately identifies security-relevant vulnerabilities than conventional OPF-surrogate-model-only explainability approaches.",
      "first_seen_at": "2026-10-30T08:45:56Z",
      "discovery_sources": [],
      "authors": [
        "S. M. Monzurul Hoque Chowdhury",
        "Qinghua Li",
        "Jianhui Wang"
      ],
      "published_at": "2026-10-28T21:31:05Z",
      "updated_at": "2026-10-28T21:31:05Z",
      "abstract": "Optimal power flow (OPF) is a foundational analysis in power system operation, and inaccuracies in OPF decision-making can result in significant operational and security consequences. With the increasing adoption of machine-learning-based OPF surrogate models for real-time and large-scale applications, power system operation has become more susceptible to cyber threats, including data manipulation and false data injection attacks. Explainable artificial intelligence (XAI), particularly Shapley value-based approaches, has been employed to quantify the influence of input features on OPF outputs. Features exhibiting high influence are commonly interpreted as vulnerable. However, this interpretation neglects the role of attack detection mechanisms in the system, which typically monitor input-to-input relationships to detect anomalous behavior. Consequently, highly influential features may be readily detectable and may not represent the most vulnerable attack surfaces. To address this limitation, we propose a defense-aware explainable AI framework that explicitly incorporates attack detectability alongside operational impact. The framework integrates an OPF surrogate model representing input-to-output dependencies with a vanilla autoencoder representing input-to-input dependencies. Shapley-based explainability is applied to both models to construct a feature-level vulnerability metric. Empirical evaluation on IEEE test systems demonstrates that the proposed framework more accurately identifies security-relevant vulnerabilities than conventional OPF-surrogate-model-only explainability approaches.",
      "source_url": "https://arxiv.org/abs/2610.38945",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-05T09:38:48Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "Robot",
            "robot manipulation"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulators"
          ]
        }
      },
      "id": "auto-arxiv-2610-28613",
      "arxiv_id": "2610.28613",
      "title": "Physical Canvas: A Vision Tactile World Model For Robot Policy Evaluation And Learning",
      "summary_ko": "Evaluating and post-training robot policies through physical interaction is costly and difficult to scale, motivating action-conditioned world models as interactive simulators. However, visually plausible predictions can misrepresent contact dynamics, providing unreliable feedback for policy evaluation and learning. We introduce PhysCanvas, a state-consistent visuo-tactile world model for contact-rich robot manipulation. PhysCanvas unifies multi-view RGB, tactile images, marker displacement, task state, and actions within a unified vision space. All modalities share a video VAE and pretrained generative backbone, allowing optical touch to reuse visual and temporal priors. Motion images derived through forward kinematics and camera projection provide spatially aligned action conditioning, while an initial RGB anchor and updated reference observations guide sequential generation. Our consistency-aware post-training uses the frozen Scene Reward Model, Modality Reward Model, and State Reward Model to supervise scene preservation, visual-tactile compatibility, and agreement with real task outcomes. The resulting world model supports closed-loop policy rollouts for evaluation and provides imagined interactions and rewards for GRPO policy updates. Across four real-world manipulation tasks, when policies are rolled out in the world model, State Reward Model supervision increases the arithmetic mean of task-wise Pearson correlations between world-model and real-world success rates from 0.602 to 0.885. Policy post-training in the reward-guided world model further increases $π_{0.5}$'s mean real-world success rate from a 59.1% SFT baseline to 70.8%. These results demonstrate the value of unified multimodal modeling and consistency-aware post-training for robot policy evaluation and learning. Project page: https://zebinx.github.io/PhysCanvas_HomePage/.",
      "first_seen_at": "2026-10-21T07:48:46Z",
      "discovery_sources": [],
      "authors": [
        "Zebin Xing",
        "Yuhang Zheng",
        "Yupeng Zheng",
        "Yichen Zhang",
        "Zichen Li",
        "Shangqing Zhou",
        "Yujie Zang",
        "Yuchen Liu",
        "Zhongpu Xia",
        "Haoran Li",
        "Qichao Zhang"
      ],
      "published_at": "2026-10-20T17:54:03Z",
      "updated_at": "2026-10-20T17:54:03Z",
      "abstract": "Evaluating and post-training robot policies through physical interaction is costly and difficult to scale, motivating action-conditioned world models as interactive simulators. However, visually plausible predictions can misrepresent contact dynamics, providing unreliable feedback for policy evaluation and learning. We introduce PhysCanvas, a state-consistent visuo-tactile world model for contact-rich robot manipulation. PhysCanvas unifies multi-view RGB, tactile images, marker displacement, task state, and actions within a unified vision space. All modalities share a video VAE and pretrained generative backbone, allowing optical touch to reuse visual and temporal priors. Motion images derived through forward kinematics and camera projection provide spatially aligned action conditioning, while an initial RGB anchor and updated reference observations guide sequential generation. Our consistency-aware post-training uses the frozen Scene Reward Model, Modality Reward Model, and State Reward Model to supervise scene preservation, visual-tactile compatibility, and agreement with real task outcomes. The resulting world model supports closed-loop policy rollouts for evaluation and provides imagined interactions and rewards for GRPO policy updates. Across four real-world manipulation tasks, when policies are rolled out in the world model, State Reward Model supervision increases the arithmetic mean of task-wise Pearson correlations between world-model and real-world success rates from 0.602 to 0.885. Policy post-training in the reward-guided world model further increases $π_{0.5}$'s mean real-world success rate from a 59.1% SFT baseline to 70.8%. These results demonstrate the value of unified multimodal modeling and consistency-aware post-training for robot policy evaluation and learning. Project page: https://zebinx.github.io/PhysCanvas_HomePage/.",
      "source_url": "https://arxiv.org/abs/2610.28613",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-05T09:38:48Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "Power Systems"
          ]
        },
        "technique_matches": {
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-37857",
      "arxiv_id": "2610.37857",
      "title": "A Community Impact-Centered Network Reduction Method for Power Systems Resilience Planning",
      "summary_ko": "Power systems resilience planning requires evaluating many hazard scenarios and investment portfolios, often requiring network reduction for computational tractability. The resulting aggregation, however, can obscure community impact data needed for equitable planning. We present a network reduction method that jointly preserves network physics and the spatial distribution of community data. We embed a household-level scarce resource score that characterizes outage impact within optimization-based Kron reductions with two formulations: a scarcity-weighted method that prioritizes vulnerable nodes, and a scarcity-constrained method that bounds data dissimilarity within aggregated clusters. Both are evaluated on a realistic distribution feeder of Galveston Island, Texas and benchmarked against multiple approaches. The scarcity-constrained method performs best, achieving 86% reduction with worst-case voltage errors below 0.01p.u. while preserving the spatial distribution of community data. By explicitly controlling data heterogeneity within and across clusters, the proposed method yields reduced networks that retain decision-relevant community information without sacrificing electrical accuracy.",
      "first_seen_at": "2026-10-30T08:45:56Z",
      "discovery_sources": [],
      "authors": [
        "Jiarui Gao",
        "Abigail L. Beck",
        "Rabab Haider"
      ],
      "published_at": "2026-10-27T19:02:38Z",
      "updated_at": "2026-10-27T19:02:38Z",
      "abstract": "Power systems resilience planning requires evaluating many hazard scenarios and investment portfolios, often requiring network reduction for computational tractability. The resulting aggregation, however, can obscure community impact data needed for equitable planning. We present a network reduction method that jointly preserves network physics and the spatial distribution of community data. We embed a household-level scarce resource score that characterizes outage impact within optimization-based Kron reductions with two formulations: a scarcity-weighted method that prioritizes vulnerable nodes, and a scarcity-constrained method that bounds data dissimilarity within aggregated clusters. Both are evaluated on a realistic distribution feeder of Galveston Island, Texas and benchmarked against multiple approaches. The scarcity-constrained method performs best, achieving 86% reduction with worst-case voltage errors below 0.01p.u. while preserving the spatial distribution of community data. By explicitly controlling data heterogeneity within and across clusters, the proposed method yields reduced networks that retain decision-relevant community information without sacrificing electrical accuracy.",
      "source_url": "https://arxiv.org/abs/2610.37857",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "우주",
        "로봇"
      ],
      "techniques": [
        "AI"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-05T09:38:48Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "우주": [
            "satellite"
          ],
          "로봇": [
            "robot",
            "robots"
          ]
        },
        "technique_matches": {
          "AI": [
            "reinforcement learning"
          ]
        }
      },
      "id": "auto-arxiv-2611-05129",
      "arxiv_id": "2611.05129",
      "title": "WayTrace: A Map-to-Language-to-Trace Hierarchy for Embodied Urban Navigation",
      "summary_ko": "Web services provide walking directions for nearly every city, but following them reliably over long routes remains challenging for autonomous robots. Point-goal navigation policies rely heavily on accurate GNSS, while most vision-language navigation models struggle to execute even short instructions outdoors. We present WayTrace, a hierarchical system that connects web-map directions to robot motion through language. An off-the-shelf orchestrator vision-language model reads the turn-by-turn directions, a satellite map and recent camera images, and issues one atomic language command at a time, such as \"cross the road to the left\". An onboard trace predictor, trained on over one million automatically labelled samples from street-level images, draws the path for this command in the image, and a reinforcement learning policy tracks it. In long-range simulated missions, WayTrace remains robust to GNSS noise and rarely leaves crosswalks or sidewalks, while point-goal baselines degrade sharply with about ten times more violations. Deployed on a wheeled-legged robot, WayTrace completes real-world missions of up to 1.37 km at about 1.1 m/s through a busy city centre, rural trails and a university campus, handling crosswalks, staircases and construction sites. Project page: https://way-trace.github.io",
      "first_seen_at": "2026-11-04T09:29:09Z",
      "discovery_sources": [],
      "authors": [
        "Manthan Patel",
        "Jonas Frey",
        "Pascal Roth",
        "Marco Hutter",
        "Cesar Cadena"
      ],
      "published_at": "2026-11-03T16:13:42Z",
      "updated_at": "2026-11-03T16:13:42Z",
      "abstract": "Web services provide walking directions for nearly every city, but following them reliably over long routes remains challenging for autonomous robots. Point-goal navigation policies rely heavily on accurate GNSS, while most vision-language navigation models struggle to execute even short instructions outdoors. We present WayTrace, a hierarchical system that connects web-map directions to robot motion through language. An off-the-shelf orchestrator vision-language model reads the turn-by-turn directions, a satellite map and recent camera images, and issues one atomic language command at a time, such as \"cross the road to the left\". An onboard trace predictor, trained on over one million automatically labelled samples from street-level images, draws the path for this command in the image, and a reinforcement learning policy tracks it. In long-range simulated missions, WayTrace remains robust to GNSS noise and rarely leaves crosswalks or sidewalks, while point-goal baselines degrade sharply with about ten times more violations. Deployed on a wheeled-legged robot, WayTrace completes real-world missions of up to 1.37 km at about 1.1 m/s through a busy city centre, rural trails and a university campus, handling crosswalks, staircases and construction sites. Project page: https://way-trace.github.io",
      "source_url": "https://arxiv.org/abs/2611.05129",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "제조"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-05T09:38:48Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "제조": [
            "manufacturing"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "Simulation"
          ]
        }
      },
      "id": "auto-arxiv-2611-04364",
      "arxiv_id": "2611.04364",
      "title": "Analytically Calibrated Upper L-Statistics for High-Dimensional Mean Change-Point Testing",
      "summary_ko": "This paper develops upper L-statistic methods for testing and localizing a high-dimensional mean change. At each candidate split, the proposed statistics rank the squared studentized CUSUM values across coordinates and aggregate either a fixed number or a fixed proportion of the largest entries. This construction connects max-type and sum-type procedures and provides sensitivity to a wide range of sparsity regimes. We establish asymptotic null distributions under an independent-component representation allowing weak cross-sectional dependence. The limiting laws are Poisson tangent processes for fixed ranks and Gaussian-process or Gumbel laws for proportional ranks. To improve finite-sample performance, we develop analytic calibrations for marginal variance estimation, cross-sectional dependence, extreme clustering, and skewness, and establish their first-order validity. We combine calibrated p-values from a matched pair of fixed- and proportional-order statistics using the Cauchy combination rule to adapt to unknown sparsity. For localization, the estimated change point is selected from the component scan with the smallest p-value. Simulation studies demonstrate generally satisfactory size calibration, competitive detection power, and effective localization across different sparsity levels. An application to semiconductor manufacturing data further illustrates the practical utility of the proposed methods.",
      "first_seen_at": "2026-11-04T09:29:09Z",
      "discovery_sources": [],
      "authors": [
        "Guowei Yan",
        "Dan Zhuang",
        "Long Feng"
      ],
      "published_at": "2026-11-03T05:49:51Z",
      "updated_at": "2026-11-03T05:49:51Z",
      "abstract": "This paper develops upper L-statistic methods for testing and localizing a high-dimensional mean change. At each candidate split, the proposed statistics rank the squared studentized CUSUM values across coordinates and aggregate either a fixed number or a fixed proportion of the largest entries. This construction connects max-type and sum-type procedures and provides sensitivity to a wide range of sparsity regimes. We establish asymptotic null distributions under an independent-component representation allowing weak cross-sectional dependence. The limiting laws are Poisson tangent processes for fixed ranks and Gaussian-process or Gumbel laws for proportional ranks. To improve finite-sample performance, we develop analytic calibrations for marginal variance estimation, cross-sectional dependence, extreme clustering, and skewness, and establish their first-order validity. We combine calibrated p-values from a matched pair of fixed- and proportional-order statistics using the Cauchy combination rule to adapt to unknown sparsity. For localization, the estimated change point is selected from the component scan with the smallest p-value. Simulation studies demonstrate generally satisfactory size calibration, competitive detection power, and effective localization across different sparsity levels. An application to semiconductor manufacturing data further illustrates the practical utility of the proposed methods.",
      "source_url": "https://arxiv.org/abs/2611.04364",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "로봇",
        "건설"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-05T09:38:48Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robotic"
          ],
          "건설": [
            "construction-site"
          ]
        },
        "technique_matches": {
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2611-05084",
      "arxiv_id": "2611.05084",
      "title": "Sparse Anchors for Visual Multi-Session Registration in Changing Environments",
      "summary_ko": "Repeated robotic monitoring requires localization across visits to the same environment, even if the environment changes. Such changes can substantially reduce persistent visual content, and repetitive structures can lead to incorrect cross-session correspondences. In this paper, we address visual multi-session registration of a new data recording (the query session) to a previously reconstructed reference map. Under substantial visual and structural changes, cross-session localization may be possible only for parts of the trajectory. In our approach, we therefore rely on a sparse set of reliable absolute pose constraints and combine them with within-session relative-pose information to register the complete trajectory to the reference map. We first estimate absolute alignment hypotheses through visual place recognition, local feature matching, and geometric verification. We then use sequence consensus to reject hypotheses that are inconsistent with a common query-to-reference alignment and retain the remaining poses as absolute anchors. These anchors are refined through featuremetric alignment against a Gaussian feature field and combined with within-session relative-pose constraints in a robust pose-graph optimization. We evaluate our approach on 15 cross-session experiments from the challenging HILTI26 construction-site dataset, covering substantial scene changes and difficult-to-match environments. Our method achieves 94.4% recall within 1m and 100.0% within 5m, outperforming independent image localization, which peaks at 65.3% and 70.7%, respectively, as well as direct registration of independently reconstructed Structure-from-Motion geometry. Our results show that sparse, reliable absolute anchors can provide accurate and complete trajectory registration even when reliable cross-session correspondences are available only for part of the query session.",
      "first_seen_at": "2026-11-04T09:29:09Z",
      "discovery_sources": [],
      "authors": [
        "Yunus Talha Erzurumlu",
        "Niklas Trekel",
        "Saurabh Gupta",
        "Nico Koltermann",
        "Olga Vysotska",
        "Jens Behley",
        "Louis Wiesmann",
        "Cyrill Stachniss"
      ],
      "published_at": "2026-11-03T15:43:03Z",
      "updated_at": "2026-11-03T15:43:03Z",
      "abstract": "Repeated robotic monitoring requires localization across visits to the same environment, even if the environment changes. Such changes can substantially reduce persistent visual content, and repetitive structures can lead to incorrect cross-session correspondences. In this paper, we address visual multi-session registration of a new data recording (the query session) to a previously reconstructed reference map. Under substantial visual and structural changes, cross-session localization may be possible only for parts of the trajectory. In our approach, we therefore rely on a sparse set of reliable absolute pose constraints and combine them with within-session relative-pose information to register the complete trajectory to the reference map. We first estimate absolute alignment hypotheses through visual place recognition, local feature matching, and geometric verification. We then use sequence consensus to reject hypotheses that are inconsistent with a common query-to-reference alignment and retain the remaining poses as absolute anchors. These anchors are refined through featuremetric alignment against a Gaussian feature field and combined with within-session relative-pose constraints in a robust pose-graph optimization. We evaluate our approach on 15 cross-session experiments from the challenging HILTI26 construction-site dataset, covering substantial scene changes and difficult-to-match environments. Our method achieves 94.4% recall within 1m and 100.0% within 5m, outperforming independent image localization, which peaks at 65.3% and 70.7%, respectively, as well as direct registration of independently reconstructed Structure-from-Motion geometry. Our results show that sparse, reliable absolute anchors can provide accurate and complete trajectory registration even when reliable cross-session correspondences are available only for part of the query session.",
      "source_url": "https://arxiv.org/abs/2611.05084",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "시뮬레이션",
        "데이터 분석"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-05T09:38:48Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "energy systems"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ],
          "데이터 분석": [
            "time series"
          ]
        }
      },
      "id": "auto-arxiv-2610-41956",
      "arxiv_id": "2610.41956",
      "title": "Chronos-2: A foundation model for multivariate time series forecasting",
      "summary_ko": "Accurate forecasting with uncertainty quantification is critical in a variety of scientific applications ranging from epidemiology and economics to energy systems and earth sciences. Yet most forecasting models still need to be developed and trained separately for each domain-specific dataset. Time series foundation models (TSFMs) aim to remove this limitation and enable the application of the same model across domains through large-scale pretraining. However, most existing TSFMs treat each time series independently and therefore cannot model interactions among time series and external drivers. Here, we introduce Chronos-2, a foundation model for zero-shot forecasting that models the interactions between multivariate time series and heterogeneous covariates, which commonly arise in scientific applications. The model infers relationships among time series directly from historical context and generates probabilistic forecasts without task-specific treatment. Across various benchmarks, Chronos-2 achieves state-of-the-art accuracy on average by improving skill scores by 5.2 percentage points relative to the strongest baselines and increasing win rates by 10 percentage points. Chronos-2 is also computationally efficient, generating forecasts for 100 time series in under one second on a single mid-range GPU, 50x and 1000x faster end-to-end than the best task-specific point and probabilistic forecasters, respectively. In addition, we demonstrate its effectiveness in diverse scientific settings -- including infectious-disease forecasting, macroeconomic modeling, carbon-flux prediction, and runoff simulation -- where the model matches or surpasses specialized methods developed for each domain. These results show that a single pretrained model can capture structure shared across temporal systems.",
      "first_seen_at": "2026-11-02T09:39:37Z",
      "discovery_sources": [],
      "authors": [
        "Abdul Fatir Ansari",
        "Oleksandr Shchur",
        "Jaris Küken",
        "Andreas Auer",
        "Boran Han",
        "Pedro Mercado",
        "Syama Sundar Rangapuram",
        "Huibin Shen",
        "Lorenzo Stella",
        "Xiyuan Zhang",
        "Mononito Goswami",
        "Shubham Kapoor",
        "Danielle C. Maddix",
        "Pablo Guerron",
        "Florian Saupe",
        "Markus Reichstein",
        "Tony Hu",
        "Junming Yin",
        "Nick Erickson",
        "Prateek Mutalik Desai",
        "Hao Wang",
        "Huzefa Rangwala",
        "George Karypis",
        "Yuyang Wang",
        "Michael Bohlke-Schneider"
      ],
      "published_at": "2026-10-30T16:08:23Z",
      "updated_at": "2026-10-30T16:08:23Z",
      "abstract": "Accurate forecasting with uncertainty quantification is critical in a variety of scientific applications ranging from epidemiology and economics to energy systems and earth sciences. Yet most forecasting models still need to be developed and trained separately for each domain-specific dataset. Time series foundation models (TSFMs) aim to remove this limitation and enable the application of the same model across domains through large-scale pretraining. However, most existing TSFMs treat each time series independently and therefore cannot model interactions among time series and external drivers. Here, we introduce Chronos-2, a foundation model for zero-shot forecasting that models the interactions between multivariate time series and heterogeneous covariates, which commonly arise in scientific applications. The model infers relationships among time series directly from historical context and generates probabilistic forecasts without task-specific treatment. Across various benchmarks, Chronos-2 achieves state-of-the-art accuracy on average by improving skill scores by 5.2 percentage points relative to the strongest baselines and increasing win rates by 10 percentage points. Chronos-2 is also computationally efficient, generating forecasts for 100 time series in under one second on a single mid-range GPU, 50x and 1000x faster end-to-end than the best task-specific point and probabilistic forecasters, respectively. In addition, we demonstrate its effectiveness in diverse scientific settings -- including infectious-disease forecasting, macroeconomic modeling, carbon-flux prediction, and runoff simulation -- where the model matches or surpasses specialized methods developed for each domain. These results show that a single pretrained model can capture structure shared across temporal systems.",
      "source_url": "https://arxiv.org/abs/2610.41956",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-06T09:31:54Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robot"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2610-28594",
      "arxiv_id": "2610.28594",
      "title": "Learning the 4D World through Point Trajectory Forecasting",
      "summary_ko": "Reliable multi-view forecasting requires videos from different cameras to describe the same evolving 3D scene. To this end, we introduce Video-Point Model (VPM), a world model that conditions on multi-view video histories and a language instruction to jointly forecast future videos from arbitrary viewpoints and the 3D trajectories of persistent scene points. We extend a pretrained world model with camera-conditioned multi-view processing that integrates complementary observations across cameras, and add a trajectory head that predicts how physical points move through time. Each trajectory tracks a single point across all views, and supervising these trajectories alongside video generation therefore anchors appearance changes in every camera to a shared description of 3D motion. To support training and evaluation, we build VPM-Data, a simulation corpus pairing synchronized multi-view videos with persistent point trajectories, and VP-WorldBench, a forecasting benchmark spanning simulated and real environments. We further extend VPM to robot control by adding robot actions as a token modality in the generator. Action tokens share the point-supervised backbone and are denoised jointly with future videos, thereby grounding the policy in the same 3D-aware representation. On VP-WorldBench and existing 4D generation benchmarks, point supervision improves both video fidelity and 3D motion accuracy over a video-only counterpart, and in simulated and real-robot experiments, it also raises the manipulation success of the action-enabled model.",
      "first_seen_at": "2026-10-21T07:48:46Z",
      "discovery_sources": [],
      "authors": [
        "Byungwoo Jeon",
        "Sunghwan Hong",
        "Dohyeon Kim",
        "Jonghoon Lee",
        "Jungwoo Park",
        "Inhee Lee",
        "Doosan Baek",
        "Jinil Kim",
        "Taehyun Lim",
        "Hanbyul Joo",
        "Marc Pollefeys",
        "Saining Xie",
        "Jinwoo Shin"
      ],
      "published_at": "2026-10-20T17:45:45Z",
      "updated_at": "2026-10-20T17:45:45Z",
      "abstract": "Reliable multi-view forecasting requires videos from different cameras to describe the same evolving 3D scene. To this end, we introduce Video-Point Model (VPM), a world model that conditions on multi-view video histories and a language instruction to jointly forecast future videos from arbitrary viewpoints and the 3D trajectories of persistent scene points. We extend a pretrained world model with camera-conditioned multi-view processing that integrates complementary observations across cameras, and add a trajectory head that predicts how physical points move through time. Each trajectory tracks a single point across all views, and supervising these trajectories alongside video generation therefore anchors appearance changes in every camera to a shared description of 3D motion. To support training and evaluation, we build VPM-Data, a simulation corpus pairing synchronized multi-view videos with persistent point trajectories, and VP-WorldBench, a forecasting benchmark spanning simulated and real environments. We further extend VPM to robot control by adding robot actions as a token modality in the generator. Action tokens share the point-supervised backbone and are denoised jointly with future videos, thereby grounding the policy in the same 3D-aware representation. On VP-WorldBench and existing 4D generation benchmarks, point supervision improves both video fidelity and 3D motion accuracy over a video-only counterpart, and in simulated and real-robot experiments, it also raises the manipulation success of the action-enabled model.",
      "source_url": "https://arxiv.org/abs/2610.28594",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "우주"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-06T09:31:54Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "우주": [
            "Satellite",
            "satellites"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2610-28404",
      "arxiv_id": "2610.28404",
      "title": "ORCA: Onboard Reflective Collaborative Agents for Reliable Task Offloading in LEO Satellite Networks",
      "summary_ko": "Low Earth orbit (LEO) satellites increasingly perform time-critical onboard processing, yet heterogeneous computing capabilities, rapidly changing inter-satellite links (ISLs) and uncertain neighbor behavior make decentralized task offloading difficult under partial observations. This paper presents ORCA, an architecture of Onboard Reflective Collaborative Agents for autonomous onboard task offloading. ORCA organizes offloading decisions into a four-Agent \\emph{Perceive}$\\rightarrow$\\emph{Reason}$\\rightarrow$ \\emph{Act}$\\rightarrow$\\emph{Learn} loop. It aims to improve efficiency and reliability in dynamic, heterogeneous, and unreliable LEO satellite networks. The simulation results of a heterogeneous 60-satellite Walker-delta constellation demonstrates that ORCA delivers competitive system efficiency in cooperative settings while achieving superior task-success robustness and offloading reliability under previously unseen malicious node behavior, indicating that outcome-grounded reflection can improve the reliability of decentralized onboard offloading without centralized runtime coordination.",
      "first_seen_at": "2026-10-21T07:48:46Z",
      "discovery_sources": [],
      "authors": [
        "Yunxin Geng",
        "Shengxiang Hao",
        "Yichen Wu",
        "Haotian Liu",
        "Weiwei Jiang",
        "Lu Cao",
        "Han Meng",
        "Yuxuan Pan",
        "Zhiqing Wei"
      ],
      "published_at": "2026-10-20T15:48:06Z",
      "updated_at": "2026-10-20T15:48:06Z",
      "abstract": "Low Earth orbit (LEO) satellites increasingly perform time-critical onboard processing, yet heterogeneous computing capabilities, rapidly changing inter-satellite links (ISLs) and uncertain neighbor behavior make decentralized task offloading difficult under partial observations. This paper presents ORCA, an architecture of Onboard Reflective Collaborative Agents for autonomous onboard task offloading. ORCA organizes offloading decisions into a four-Agent \\emph{Perceive}$\\rightarrow$\\emph{Reason}$\\rightarrow$ \\emph{Act}$\\rightarrow$\\emph{Learn} loop. It aims to improve efficiency and reliability in dynamic, heterogeneous, and unreliable LEO satellite networks. The simulation results of a heterogeneous 60-satellite Walker-delta constellation demonstrates that ORCA delivers competitive system efficiency in cooperative settings while achieving superior task-success robustness and offloading reliability under previously unseen malicious node behavior, indicating that outcome-grounded reflection can improve the reliability of decentralized onboard offloading without centralized runtime coordination.",
      "source_url": "https://arxiv.org/abs/2610.28404",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-06T09:31:54Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power system"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation",
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2611-03049",
      "arxiv_id": "2611.03049",
      "title": "Adaptive Residual Modeling for Scenario Learning in Probabilistic Load Forecasting",
      "summary_ko": "Uncertainties in weather, user behavior, and distributed energy production introduce significant variability into feeder power. Effective power system operations require both multi-horizon load forecasts and well-calibrated extreme quantiles for the forecast. Nevertheless, standard objectives that fit the full forecast distribution often underemphasize rare observations and extremes. Conversely, applying heavy tail-weighting in the distribution to capture these extremes can distort the underlying probability distribution. To address this issue, this paper proposes adaptive residual modeling for scenario learning (ARMSL), a two-stage framework. Particularly, Stage I in our framework sequentially models environmental response, history-dependent load patterns, persistent residual deviations, and the remaining innovations. Upon the modeling, we then generate joint active- and reactive-power scenarios with state-dependent dispersion. Stage II keeps the scenario model fixed and calibrates extreme marginal quantiles using a simple, channel-specific reference forecast (an anchor). Only outward deviations from this anchor are admitted, with their contribution determined by local calibration history and the current operating state. From our numerical simulations on FeederBW dataset, Stage I outperforms diffusion baselines in energy and variogram scores. Our complete framework achieves an active-power 0.99-quantile pinball loss of 0.1932 (a 30.08% to 85.33% reduction relative to three baselines) with 1.054% exceedance rate against a nominal 1%. From simulations on the Rolle dataset, we observe decreased losses by 5.39%--14.40% compared to the active-power-only anchor. Furthermore, the unchanged Stage I scenarios in the simulation yield a correlation of 0.953 between predicted and observed feeder event rates, with 90.0% of feeder errors falling within two percentage points.",
      "first_seen_at": "2026-11-03T09:42:47Z",
      "discovery_sources": [],
      "authors": [
        "Jinbao Wang",
        "Jun Liu",
        "Shiliang Zhang",
        "Xuehui Ma",
        "Yushuai Li",
        "Lin Yan"
      ],
      "published_at": "2026-11-02T12:46:01Z",
      "updated_at": "2026-11-02T12:46:01Z",
      "abstract": "Uncertainties in weather, user behavior, and distributed energy production introduce significant variability into feeder power. Effective power system operations require both multi-horizon load forecasts and well-calibrated extreme quantiles for the forecast. Nevertheless, standard objectives that fit the full forecast distribution often underemphasize rare observations and extremes. Conversely, applying heavy tail-weighting in the distribution to capture these extremes can distort the underlying probability distribution. To address this issue, this paper proposes adaptive residual modeling for scenario learning (ARMSL), a two-stage framework. Particularly, Stage I in our framework sequentially models environmental response, history-dependent load patterns, persistent residual deviations, and the remaining innovations. Upon the modeling, we then generate joint active- and reactive-power scenarios with state-dependent dispersion. Stage II keeps the scenario model fixed and calibrates extreme marginal quantiles using a simple, channel-specific reference forecast (an anchor). Only outward deviations from this anchor are admitted, with their contribution determined by local calibration history and the current operating state. From our numerical simulations on FeederBW dataset, Stage I outperforms diffusion baselines in energy and variogram scores. Our complete framework achieves an active-power 0.99-quantile pinball loss of 0.1932 (a 30.08% to 85.33% reduction relative to three baselines) with 1.054% exceedance rate against a nominal 1%. From simulations on the Rolle dataset, we observe decreased losses by 5.39%--14.40% compared to the active-power-only anchor. Furthermore, the unchanged Stage I scenarios in the simulation yield a correlation of 0.953 between predicted and observed feeder event rates, with 90.0% of feeder errors falling within two percentage points.",
      "source_url": "https://arxiv.org/abs/2611.03049",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "제조"
      ],
      "techniques": [
        "시뮬레이션",
        "데이터 분석"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-06T09:31:54Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "제조": [
            "manufacturing"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ],
          "데이터 분석": [
            "point-cloud"
          ]
        }
      },
      "id": "auto-arxiv-2611-04280",
      "arxiv_id": "2611.04280",
      "title": "Bezier-GP: Bezier-Surface-Based Gaussian Process for Chips-on-Wafer Warpage Modeling",
      "summary_ko": "As the semiconductor industry transitions to the post-Moore paradigm, wafer warpage has become a critical quality issue in advanced packaging; particularly for 3D stacking and high-bandwidth memory manufacturing where die-level deformation can affect bonding quality, interconnect reliability, and yield. However, direct high-throughput measurement of individual chip warpage remains difficult in mass-production environments, while existing wafer-level metrology systems routinely provide dense 3D shape data at the wafer scale. This paper proposes a Bezier-surface-based Gaussian Process---known as Bezier-GP---for chips-on-wafer warpage modeling. The proposed framework combines wafer-level point-cloud metrology data with wafer-map layout information to construct chip-region-level Bezier surface patches, imposes continuity constraints between neighboring patches, and represents the resulting chips-on-wafer surface through a compact free-control-point vector. A GP is then introduced in this low-dimensional representation to model wafer-to-wafer variation, chip-region-level spatial dependence, and provide uncertainty quantification. Numerical experiments, using an ASC wafer-map layout, demonstrate that the proposed framework can reconstruct chips-on-wafer surfaces, preserve chip-region-level geometric interpretability, and recover population-level mean and covariance structures under a controlled multi-wafer simulation setting. The results suggest that Bezier-GP provides a measurement-driven statistical modeling foundation for connecting wafer-level metrology data to chip-region-level warpage analysis in semiconductor manufacturing.",
      "first_seen_at": "2026-11-04T09:29:09Z",
      "discovery_sources": [],
      "authors": [
        "Sangyup Lee",
        "Xiao Liu",
        "Jianjun Shi"
      ],
      "published_at": "2026-11-03T04:05:49Z",
      "updated_at": "2026-11-03T04:05:49Z",
      "abstract": "As the semiconductor industry transitions to the post-Moore paradigm, wafer warpage has become a critical quality issue in advanced packaging; particularly for 3D stacking and high-bandwidth memory manufacturing where die-level deformation can affect bonding quality, interconnect reliability, and yield. However, direct high-throughput measurement of individual chip warpage remains difficult in mass-production environments, while existing wafer-level metrology systems routinely provide dense 3D shape data at the wafer scale. This paper proposes a Bezier-surface-based Gaussian Process---known as Bezier-GP---for chips-on-wafer warpage modeling. The proposed framework combines wafer-level point-cloud metrology data with wafer-map layout information to construct chip-region-level Bezier surface patches, imposes continuity constraints between neighboring patches, and represents the resulting chips-on-wafer surface through a compact free-control-point vector. A GP is then introduced in this low-dimensional representation to model wafer-to-wafer variation, chip-region-level spatial dependence, and provide uncertainty quantification. Numerical experiments, using an ASC wafer-map layout, demonstrate that the proposed framework can reconstruct chips-on-wafer surfaces, preserve chip-region-level geometric interpretability, and recover population-level mean and covariance structures under a controlled multi-wafer simulation setting. The results suggest that Bezier-GP provides a measurement-driven statistical modeling foundation for connecting wafer-level metrology data to chip-region-level warpage analysis in semiconductor manufacturing.",
      "source_url": "https://arxiv.org/abs/2611.04280",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "AI",
        "시뮬레이션",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-06T09:31:54Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "도로교통": [
            "Traffic Signal"
          ]
        },
        "technique_matches": {
          "AI": [
            "reinforcement learning"
          ],
          "시뮬레이션": [
            "simulation",
            "simulations",
            "simulator"
          ],
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2611-06353",
      "arxiv_id": "2611.06353",
      "title": "Traffic-VL: Reinforced Vision-Language Models for Generalizable End-to-End Traffic Signal Control",
      "summary_ko": "Existing traffic signal control methods typically rely on structured traffic states produced by dedicated perception pipelines. Emerging visual approaches often use idealized simulator images and convert them into intermediate representations before control, limiting their ability to directly exploit roadside visual observations. To bridge both the visual-domain and perception-to-decision gaps, we introduce SynTra-V and Traffic-VL. SynTra-V converts physics-based traffic simulations into photorealistic multi-view surveillance imagery across diverse road topologies and sensing conditions, and automatic semantic auditing checks that control-critical geometry, vehicle movements, and lane semantics match the simulator state. Traffic-VL is an eight-billion-parameter vision-language model that directly maps synchronized multi-view images and natural-language phase descriptions to signal decisions without intermediate structured states. Building on Traffic-R1's two-stage reinforced reasoning framework, Traffic-VL adapts its vision encoder via offline reinforcement learning on SynTra-V and freezes it during online visual stepwise trajectory policy optimization in closed-loop simulation. Experiments show that Traffic-VL matches structured-state Traffic-R1 while outperforming image-based controllers and general-purpose vision-language models, including Gemini 3 Pro. Evaluations on unseen topologies, degraded sensing conditions, real surveillance footage, and a three-week deployment across ten intersections further demonstrate robust visual generalization and effective constrained online control.",
      "first_seen_at": "2026-11-05T09:38:48Z",
      "discovery_sources": [],
      "authors": [
        "Xingchen Zou",
        "Maonan Wang",
        "Kemou Jiang",
        "Jie Li",
        "Yiqi Chen",
        "Xi Chen",
        "Hai Yang",
        "Yuxuan Liang"
      ],
      "published_at": "2026-11-04T11:15:20Z",
      "updated_at": "2026-11-04T11:15:20Z",
      "abstract": "Existing traffic signal control methods typically rely on structured traffic states produced by dedicated perception pipelines. Emerging visual approaches often use idealized simulator images and convert them into intermediate representations before control, limiting their ability to directly exploit roadside visual observations. To bridge both the visual-domain and perception-to-decision gaps, we introduce SynTra-V and Traffic-VL. SynTra-V converts physics-based traffic simulations into photorealistic multi-view surveillance imagery across diverse road topologies and sensing conditions, and automatic semantic auditing checks that control-critical geometry, vehicle movements, and lane semantics match the simulator state. Traffic-VL is an eight-billion-parameter vision-language model that directly maps synchronized multi-view images and natural-language phase descriptions to signal decisions without intermediate structured states. Building on Traffic-R1's two-stage reinforced reasoning framework, Traffic-VL adapts its vision encoder via offline reinforcement learning on SynTra-V and freezes it during online visual stepwise trajectory policy optimization in closed-loop simulation. Experiments show that Traffic-VL matches structured-state Traffic-R1 while outperforming image-based controllers and general-purpose vision-language models, including Gemini 3 Pro. Evaluations on unseen topologies, degraded sensing conditions, real surveillance footage, and a three-week deployment across ten intersections further demonstrate robust visual generalization and effective constrained online control.",
      "source_url": "https://arxiv.org/abs/2611.06353",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "AI"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-06T09:31:54Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robot",
            "Robotics"
          ]
        },
        "technique_matches": {
          "AI": [
            "reinforcement learning"
          ]
        }
      },
      "id": "auto-arxiv-2610-28589",
      "arxiv_id": "2610.28589",
      "title": "AgenticRoboRL: Bringing Coding-Agent Post-Training to Agentic Robotics",
      "summary_ko": "Agentic robotics systems combine planners with robot harnesses to perform complex manipulation tasks. Recent work improves these systems through better harnesses and external memory, but relies on powerful closed-source models as planners and does not improve the planners' own capabilities. In contrast, coding agents have improved substantially by post-training the model itself. We therefore investigate whether post-training can also improve robot planners. However, applying it across robot harnesses requires accommodating different execution mechanisms, including VLA skill invocation and control-code generation. To this end, we introduce AgenticRoboRL, a framework for post-training robot planners across different harnesses. The training recipe combines supervised fine-tuning with multi-turn reinforcement learning. To accommodate different execution mechanisms, harness-specific adapters connect recorded planner interactions with task outcomes, enabling a common RL training pipeline while keeping execution components fixed. On LIBERO-PRO, AgenticRoboRL raises the success rate of a 9B-parameter planner from 18.1% to 71.0% without external memory, approaching the 72.1% achieved by Codex (GPT-5.5) with external memory. These results demonstrate post-training as an effective approach for improving robot planners. We will release the framework and trained planner checkpoints.",
      "first_seen_at": "2026-10-21T07:48:46Z",
      "discovery_sources": [],
      "authors": [
        "Xinxin Liu",
        "Bingxin Xu",
        "Wenrui Bao",
        "Ming Li",
        "Wentao Bao",
        "Chen Chen",
        "Yuzhang Shang"
      ],
      "published_at": "2026-10-20T17:41:09Z",
      "updated_at": "2026-10-21T18:28:40Z",
      "abstract": "Agentic robotics systems combine planners with robot harnesses to perform complex manipulation tasks. Recent work improves these systems through better harnesses and external memory, but relies on powerful closed-source models as planners and does not improve the planners' own capabilities. In contrast, coding agents have improved substantially by post-training the model itself. We therefore investigate whether post-training can also improve robot planners. However, applying it across robot harnesses requires accommodating different execution mechanisms, including VLA skill invocation and control-code generation. To this end, we introduce AgenticRoboRL, a framework for post-training robot planners across different harnesses. The training recipe combines supervised fine-tuning with multi-turn reinforcement learning. To accommodate different execution mechanisms, harness-specific adapters connect recorded planner interactions with task outcomes, enabling a common RL training pipeline while keeping execution components fixed. On LIBERO-PRO, AgenticRoboRL raises the success rate of a 9B-parameter planner from 18.1% to 71.0% without external memory, approaching the 72.1% achieved by Codex (GPT-5.5) with external memory. These results demonstrate post-training as an effective approach for improving robot planners. We will release the framework and trained planner checkpoints.",
      "source_url": "https://arxiv.org/abs/2610.28589",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "AI",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-07T08:56:39Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "Robot",
            "robots",
            "Humanoid"
          ]
        },
        "technique_matches": {
          "AI": [
            "reinforcement learning"
          ],
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-28470",
      "arxiv_id": "2610.28470",
      "title": "Fast Motion Retargeting Pipeline for Scalable Humanoid Robot Learning",
      "summary_ko": "While large-scale human motion capture datasets provide a rich source of demonstrations for humanoid robot learning, retargeting the diverse motions to different humanoid robots remains a nontrivial challenge. Existing kinodynamic trajectory optimization pipelines are computationally expensive and often require manual adjustments per motion. We present FRDV (Fast Retargeting with Dynamics Validation), a scalable framework consisting of rapid constrained kinematic retargeting and an additional dynamics validation pipeline. Its kinematic stage integrates a custom Sequential Quadratic Programming solver, Real-Time Iteration, and parallel chunk-based processing, achieving up to 9,156 frames per second, approximately up to 136 times faster than GMR and ProtoMotions, while robot-specific constraints are incorporated into the optimization to reduce common retargeting artifacts, such as ground penetration and self-collision. For motions requiring additional verification, a contact implicit iLQR controller is utilized to test the retargeted reference under full rigid body dynamics. This dynamics-based validation identifies dynamically infeasible motions and refines contact-related artifacts such as foot floating and foot skating. We evaluate retargeting quality by training motion tracking reinforcement learning policies using several state-of-the-art pipelines and comparing their tracking fidelity and success rates. Furthermore, sim-to-real transfer is demonstrated on a Unitree G1 humanoid. Videos and code are available at: https://daroslab.github.io/frdv/.",
      "first_seen_at": "2026-10-21T07:48:46Z",
      "discovery_sources": [],
      "authors": [
        "Daniel Marew",
        "Sangjun Park",
        "Nisal Perera",
        "Donghyun Kim"
      ],
      "published_at": "2026-10-20T16:22:00Z",
      "updated_at": "2026-10-20T16:22:00Z",
      "abstract": "While large-scale human motion capture datasets provide a rich source of demonstrations for humanoid robot learning, retargeting the diverse motions to different humanoid robots remains a nontrivial challenge. Existing kinodynamic trajectory optimization pipelines are computationally expensive and often require manual adjustments per motion. We present FRDV (Fast Retargeting with Dynamics Validation), a scalable framework consisting of rapid constrained kinematic retargeting and an additional dynamics validation pipeline. Its kinematic stage integrates a custom Sequential Quadratic Programming solver, Real-Time Iteration, and parallel chunk-based processing, achieving up to 9,156 frames per second, approximately up to 136 times faster than GMR and ProtoMotions, while robot-specific constraints are incorporated into the optimization to reduce common retargeting artifacts, such as ground penetration and self-collision. For motions requiring additional verification, a contact implicit iLQR controller is utilized to test the retargeted reference under full rigid body dynamics. This dynamics-based validation identifies dynamically infeasible motions and refines contact-related artifacts such as foot floating and foot skating. We evaluate retargeting quality by training motion tracking reinforcement learning policies using several state-of-the-art pipelines and comparing their tracking fidelity and success rates. Furthermore, sim-to-real transfer is demonstrated on a Unitree G1 humanoid. Videos and code are available at: https://daroslab.github.io/frdv/.",
      "source_url": "https://arxiv.org/abs/2610.28470",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "우주"
      ],
      "techniques": [
        "AI",
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-07T08:56:39Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "우주": [
            "Satellite",
            "satellites"
          ]
        },
        "technique_matches": {
          "AI": [
            "Reinforcement Learning"
          ],
          "시뮬레이션": [
            "Simulation"
          ]
        }
      },
      "id": "auto-arxiv-2610-28248",
      "arxiv_id": "2610.28248",
      "title": "Interference-Graph-Aided Multi-Agent Reinforcement Learning for Beam Hopping LEO Satellite Networks",
      "summary_ko": "Beam hopping technology enables LEO satellites to dynamically switch beam coverage positions, providing flexible resource allocation for non-uniformly distributed traffic demands. However, the severe spatial interference coupling among beams makes coordinated scheduling challenging. To address this problem, in this letter, we propose an interference-graph-aided multi-agent reinforcement learning approach that constructs an interference graph to capture beam coupling relationships and injects this structural prior into agent-side representation learning. Each beam agent aggregates information from interference neighbors via graph convolution before estimating local Q-values, which are then decomposed by the standard mixing network. Simulation results demonstrate that the proposed method achieves 34.4\\% higher throughput than QMIX with more stable interference management, under non-uniform traffic and co-channel interference settings.",
      "first_seen_at": "2026-10-21T07:48:46Z",
      "discovery_sources": [],
      "authors": [
        "Hao Yuan",
        "Ziyi Yang",
        "Xing Zhang"
      ],
      "published_at": "2026-10-20T14:07:32Z",
      "updated_at": "2026-10-20T14:07:32Z",
      "abstract": "Beam hopping technology enables LEO satellites to dynamically switch beam coverage positions, providing flexible resource allocation for non-uniformly distributed traffic demands. However, the severe spatial interference coupling among beams makes coordinated scheduling challenging. To address this problem, in this letter, we propose an interference-graph-aided multi-agent reinforcement learning approach that constructs an interference graph to capture beam coupling relationships and injects this structural prior into agent-side representation learning. Each beam agent aggregates information from interference neighbors via graph convolution before estimating local Q-values, which are then decomposed by the standard mixing network. Simulation results demonstrate that the proposed method achieves 34.4\\% higher throughput than QMIX with more stable interference management, under non-uniform traffic and co-channel interference settings.",
      "source_url": "https://arxiv.org/abs/2610.28248",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "AI",
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-07T08:56:39Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power-system",
            "Power Systems"
          ]
        },
        "technique_matches": {
          "AI": [
            "AI",
            "Artificial Intelligence"
          ],
          "시뮬레이션": [
            "Simulation",
            "simulators"
          ]
        }
      },
      "id": "auto-arxiv-2611-02711",
      "arxiv_id": "2611.02711",
      "title": "Human-Supervised Artificial Intelligence Researchers in Electric Power Systems: Knowledge, Physics, Simulation, and Governance for Large Language Models",
      "summary_ko": "Conclusions in power-system research require a clear evidential basis. Researchers need to understand the data sources, modelling assumptions, and solution procedures to assess the conditions under which a conclusion holds. Large language models (LLMs) can help researchers extract information from documents and translate research requests into code and tool calls, but their outputs still require physical validation. Focusing on human-supervised artificial intelligence (AI) researchers, this review examines power-system LLM applications and related scientific workflows, together with physics-grounded power AI studies, to discuss the conditions for using LLMs in scientific research. In the proposed knowledge-physics-simulation-governance framework, LLMs translate information and coordinate tasks, simulators and validation tools test models, and experts retain authority over critical assumptions, safety boundaries, and final conclusions. Research processes should preserve materials for review so that claims can be traced to their sources, computations can be reproduced, and failed attempts and approvals can be examined. On this basis, a staged development roadmap and requirements for research reporting are proposed. Evaluation should focus on research artifacts and their validation to assess whether LLMs can improve the efficiency of power-system research within clearly defined permissions.",
      "first_seen_at": "2026-11-03T09:42:47Z",
      "discovery_sources": [],
      "authors": [
        "Xiangrui Meng",
        "Jingcheng Liu",
        "Ke Wang",
        "Miaopeng Yu",
        "Zhengping Lin"
      ],
      "published_at": "2026-11-02T08:03:54Z",
      "updated_at": "2026-11-02T08:03:54Z",
      "abstract": "Conclusions in power-system research require a clear evidential basis. Researchers need to understand the data sources, modelling assumptions, and solution procedures to assess the conditions under which a conclusion holds. Large language models (LLMs) can help researchers extract information from documents and translate research requests into code and tool calls, but their outputs still require physical validation. Focusing on human-supervised artificial intelligence (AI) researchers, this review examines power-system LLM applications and related scientific workflows, together with physics-grounded power AI studies, to discuss the conditions for using LLMs in scientific research. In the proposed knowledge-physics-simulation-governance framework, LLMs translate information and coordinate tasks, simulators and validation tools test models, and experts retain authority over critical assumptions, safety boundaries, and final conclusions. Research processes should preserve materials for review so that claims can be traced to their sources, computations can be reproduced, and failed attempts and approvals can be examined. On this basis, a staged development roadmap and requirements for research reporting are proposed. Evaluation should focus on research artifacts and their validation to assess whether LLMs can improve the efficiency of power-system research within clearly defined permissions.",
      "source_url": "https://arxiv.org/abs/2611.02711",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "제조"
      ],
      "techniques": [
        "시뮬레이션",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-07T08:56:39Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "제조": [
            "manufacturing"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ],
          "최적화": [
            "optimization",
            "optimizer"
          ]
        }
      },
      "id": "auto-arxiv-2611-04251",
      "arxiv_id": "2611.04251",
      "title": "Differentiable topology optimization for high-fidelity acoustic holography beyond the thin-element approximation",
      "summary_ko": "Acoustic holography in the megahertz range can impact applications from manufacturing and non-destructive testing to transcranial ultrasound. Designing lens topologies for complex holograms in the sub-megahertz to low-megahertz regime most relevant to transcranial therapy is difficult because wave propagation through an acoustically thick lens cannot be ignored. Here we show that the heterogeneous angular spectrum approach (HASA), which admits in-plane varying speed-of-sound maps and supports rapid differentiable optimization with the Adam optimizer, can generate three-dimensional lens topologies directly. With it we quantify the failure of the thin-element approximation (TEA) at these frequencies, where lenses enter an acoustically thick regime (thickness L much greater than the wavelength) governed by refractive walk-off, and we introduce a dimensionless refractive walk-off number Nw that defines a frequency-dependent operating regime. Topology optimization outperformed TEA-based design in all 45 configurations tested, with the largest structural gains at or below 1 MHz; for high-numerical-aperture designs the fidelity at 1 MHz matched that at 2 MHz, while structural similarity was highest at 0.5 MHz. The sub-megahertz to low-megahertz window is therefore a favorable operating regime for high-fidelity transcranial holography rather than one to be avoided. Free-field hydrophone measurements of 3D-printed lenses reproduced the designed patterns, and because the framework also accounts for aberration in heterogeneous media, patient-specific lenses corrected skull-induced aberration in simulation and ex vivo experiment, generalized across skull segments, and shaped volumetric beams through the human skull. Together these results provide a framework for designing simple, economical, and high-performance ultrasound systems.",
      "first_seen_at": "2026-11-04T09:29:09Z",
      "discovery_sources": [],
      "authors": [
        "Pradosh Pritam Dash",
        "Costas D. Arvanitis"
      ],
      "published_at": "2026-11-03T03:39:55Z",
      "updated_at": "2026-11-03T03:39:55Z",
      "abstract": "Acoustic holography in the megahertz range can impact applications from manufacturing and non-destructive testing to transcranial ultrasound. Designing lens topologies for complex holograms in the sub-megahertz to low-megahertz regime most relevant to transcranial therapy is difficult because wave propagation through an acoustically thick lens cannot be ignored. Here we show that the heterogeneous angular spectrum approach (HASA), which admits in-plane varying speed-of-sound maps and supports rapid differentiable optimization with the Adam optimizer, can generate three-dimensional lens topologies directly. With it we quantify the failure of the thin-element approximation (TEA) at these frequencies, where lenses enter an acoustically thick regime (thickness L much greater than the wavelength) governed by refractive walk-off, and we introduce a dimensionless refractive walk-off number Nw that defines a frequency-dependent operating regime. Topology optimization outperformed TEA-based design in all 45 configurations tested, with the largest structural gains at or below 1 MHz; for high-numerical-aperture designs the fidelity at 1 MHz matched that at 2 MHz, while structural similarity was highest at 0.5 MHz. The sub-megahertz to low-megahertz window is therefore a favorable operating regime for high-fidelity transcranial holography rather than one to be avoided. Free-field hydrophone measurements of 3D-printed lenses reproduced the designed patterns, and because the framework also accounts for aberration in heterogeneous media, patient-specific lenses corrected skull-induced aberration in simulation and ex vivo experiment, generalized across skull segments, and shaped volumetric beams through the human skull. Together these results provide a framework for designing simple, economical, and high-performance ultrasound systems.",
      "source_url": "https://arxiv.org/abs/2611.04251",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "물류"
      ],
      "techniques": [
        "AI"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-07T08:56:39Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "물류": [
            "logistics"
          ]
        },
        "technique_matches": {
          "AI": [
            "AI"
          ]
        }
      },
      "id": "auto-arxiv-2611-08312",
      "arxiv_id": "2611.08312",
      "title": "Trilemma: How Agents choose between Honesty, Confidentiality, and Task Completion",
      "summary_ko": "Personal AI agents often act on underspecified instructions and draw on context such as their user's messages and calendar to decide how to proceed. This context can contain private preferences that interfere with the task, with other people's interests, or with confidentiality norms. Prior work has studied deception under goal conflicts and the leakage of private information, but largely as separate problems. We study both problems together in a simulated company Slack workspace in which four employees delegate sprint staffing to personal agents. Two agents learn from private messages that their employee avoids working with a particular colleague. Because the other agents request justifications for task preferences and the only true justification is confidential, the agents cannot simultaneously remain honest, preserve confidentiality, and complete the staffing. We evaluate seven open-weight models and Claude and GPT models. Of the 135/140 open-weight agents that pursue the avoidance preference, 107 either make false claims about task fit or logistics or cite a true consideration as if it were their actual reason. In a three-model comparison, confidentiality instructions reduce disclosure of the preference from 83% (none) to 31% (medium) to 2% (strong). However, the task is often not completed anyway; in the main experiment (medium confidentiality instructions), only 16 of 70 runs end with a valid staffing. In their reasoning traces, agents frequently acknowledge evidence against their claims and still conclude that the claims are not false; they engage in motivated reasoning. Our results indicate that agents can strategically make false or misleading claims in a day-to-day task without being instructed to; the environment provides a controlled testbed for studying how agents resolve such conflicts and for developing deception monitors.",
      "first_seen_at": "2026-11-06T09:31:54Z",
      "discovery_sources": [],
      "authors": [
        "Johannes Taraz",
        "Abhinav Kumar",
        "Sahar Abdelnabi"
      ],
      "published_at": "2026-11-05T17:12:32Z",
      "updated_at": "2026-11-05T17:12:32Z",
      "abstract": "Personal AI agents often act on underspecified instructions and draw on context such as their user's messages and calendar to decide how to proceed. This context can contain private preferences that interfere with the task, with other people's interests, or with confidentiality norms. Prior work has studied deception under goal conflicts and the leakage of private information, but largely as separate problems. We study both problems together in a simulated company Slack workspace in which four employees delegate sprint staffing to personal agents. Two agents learn from private messages that their employee avoids working with a particular colleague. Because the other agents request justifications for task preferences and the only true justification is confidential, the agents cannot simultaneously remain honest, preserve confidentiality, and complete the staffing. We evaluate seven open-weight models and Claude and GPT models. Of the 135/140 open-weight agents that pursue the avoidance preference, 107 either make false claims about task fit or logistics or cite a true consideration as if it were their actual reason. In a three-model comparison, confidentiality instructions reduce disclosure of the preference from 83% (none) to 31% (medium) to 2% (strong). However, the task is often not completed anyway; in the main experiment (medium confidentiality instructions), only 16 of 70 runs end with a valid staffing. In their reasoning traces, agents frequently acknowledge evidence against their claims and still conclude that the claims are not false; they engage in motivated reasoning. Our results indicate that agents can strategically make false or misleading claims in a day-to-day task without being instructed to; the environment provides a controlled testbed for studying how agents resolve such conflicts and for developing deception monitors.",
      "source_url": "https://arxiv.org/abs/2611.08312",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "도로교통"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-07T08:56:39Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "도로교통": [
            "road traffic"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation",
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2611-07995",
      "arxiv_id": "2611.07995",
      "title": "Leveraging deep generative modelling at the agent level in discrete-event simulations",
      "summary_ko": "Emulators are often invoked to facilitate parameter inference in computationally intensive agent-based simulations (ABSs). Most approaches, however, require a large sample of pre-existing outputs on which such emulators can be trained. For computationally intensive simulations, this sample can be prohibitively expensive to generate. We propose a new approach to leverage emulators at the agent level in event-based ABSs to reduce the computational requirements of the simulation itself. We demonstrate this in a computationally intensive ``multi-disease'' ABS, Thanzi La Onse, which self-consistently simulates a wide range of medical conditions in a population. By applying conditional tabular generative adversarial networks to replace the modelling of one of the simulated causes of ill health, road traffic injuries (RTIs), we are able to improve its simulation run-time by 86.6% (95% bootstrap confidence interval [85.2, 88.1]), while recovering the evolution of cause-specific mean disability adjusted life years with a 13% error, despite the low assumed incidence of fatal RTIs (~2%). Crucially, we do so while retaining the full ABS framework, and correctly recovering multi-disease dynamics. This represents a crucial step towards enabling full-model emulators for very computationally intensive simulations, and making large ABS amenable to rigorous parameter inference and a full understanding of parameter sensitivity.",
      "first_seen_at": "2026-11-06T09:31:54Z",
      "discovery_sources": [],
      "authors": [
        "Margherita Molaro",
        "Matthew M. Graham",
        "Oliver Watson",
        "Rachel E. Murray-Watson",
        "Martin Chalkley",
        "Joseph H. Collins",
        "Bingling She",
        "Asif U. Tamuri",
        "Timothy B. Hallett"
      ],
      "published_at": "2026-11-05T13:23:39Z",
      "updated_at": "2026-11-05T13:23:39Z",
      "abstract": "Emulators are often invoked to facilitate parameter inference in computationally intensive agent-based simulations (ABSs). Most approaches, however, require a large sample of pre-existing outputs on which such emulators can be trained. For computationally intensive simulations, this sample can be prohibitively expensive to generate. We propose a new approach to leverage emulators at the agent level in event-based ABSs to reduce the computational requirements of the simulation itself. We demonstrate this in a computationally intensive ``multi-disease'' ABS, Thanzi La Onse, which self-consistently simulates a wide range of medical conditions in a population. By applying conditional tabular generative adversarial networks to replace the modelling of one of the simulated causes of ill health, road traffic injuries (RTIs), we are able to improve its simulation run-time by 86.6% (95% bootstrap confidence interval [85.2, 88.1]), while recovering the evolution of cause-specific mean disability adjusted life years with a 13% error, despite the low assumed incidence of fatal RTIs (~2%). Crucially, we do so while retaining the full ABS framework, and correctly recovering multi-disease dynamics. This represents a crucial step towards enabling full-model emulators for very computationally intensive simulations, and making large ABS amenable to rigorous parameter inference and a full understanding of parameter sensitivity.",
      "source_url": "https://arxiv.org/abs/2611.07995",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-08T09:11:53Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power systems"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2609-39719",
      "arxiv_id": "2609.39719",
      "title": "Transient Triggering Grid-Forming Synchronization Control Under Voltage and Frequency Dips",
      "summary_ko": "Grid-forming (GFM) inverters are gaining attention as a promising alternative for conventional synchronous generators in the modern power systems. Unlike conventional synchronous generators, GFM inverters have limited overcurrent capability that makes them vulnerable during large disturbances. During disturbances i.e., voltage and frequency dips, GFM inverters are pushed into current-limited operation to protect the semiconductor switches. This causes the internal angle of the GFM inverters to accelerate and lose synchronism with the rest of the grid. To address this limitation, this article proposes a transient triggering grid-forming (TTGFM) synchronization control to enhance the synchronization stability performance under voltage and frequency dips. This method uses two feedback signals; terminal voltage and the difference between unsaturated and saturated power to adjust the internal angle which is generated by power synchronization loop (PSL) of the GFM inverters. These two signals manipulates the internal reference angle generation that act as a virtual braking mechanism. This mechanism limits the angle acceleration during voltage and frequency dips without requiring an extra supervisory signal or parameters to tune. The proposed method is benchmarked against two state-of-the-art synchronization enhancement schemes and validated through high-fidelity electromagnetic transient (EMT) simulations with a grid dynamic equivalent (GDE) model in MATLAB/Simulink\\textsuperscript{\\textregistered}. An analytical framework is developed to derive the synchronization instability mechanism and the critical limits of the stability margins. Generalization of the GDE model further shows that the TTGFM control is not restricted to a single configuration but is applicable to any standard benchmark system.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Dewan Mahnaaz Mahmud",
        "Vinu Thomas",
        "Bogdan Marinescu",
        "Mickaël Hilairet"
      ],
      "published_at": "2026-09-30T13:29:25Z",
      "updated_at": "2026-09-30T13:29:25Z",
      "abstract": "Grid-forming (GFM) inverters are gaining attention as a promising alternative for conventional synchronous generators in the modern power systems. Unlike conventional synchronous generators, GFM inverters have limited overcurrent capability that makes them vulnerable during large disturbances. During disturbances i.e., voltage and frequency dips, GFM inverters are pushed into current-limited operation to protect the semiconductor switches. This causes the internal angle of the GFM inverters to accelerate and lose synchronism with the rest of the grid. To address this limitation, this article proposes a transient triggering grid-forming (TTGFM) synchronization control to enhance the synchronization stability performance under voltage and frequency dips. This method uses two feedback signals; terminal voltage and the difference between unsaturated and saturated power to adjust the internal angle which is generated by power synchronization loop (PSL) of the GFM inverters. These two signals manipulates the internal reference angle generation that act as a virtual braking mechanism. This mechanism limits the angle acceleration during voltage and frequency dips without requiring an extra supervisory signal or parameters to tune. The proposed method is benchmarked against two state-of-the-art synchronization enhancement schemes and validated through high-fidelity electromagnetic transient (EMT) simulations with a grid dynamic equivalent (GDE) model in MATLAB/Simulink\\textsuperscript{\\textregistered}. An analytical framework is developed to derive the synchronization instability mechanism and the critical limits of the stability margins. Generalization of the GDE model further shows that the TTGFM control is not restricted to a single configuration but is applicable to any standard benchmark system.",
      "source_url": "https://arxiv.org/abs/2609.39719",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "철도"
      ],
      "techniques": [
        "AI",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-08T09:11:53Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "철도": [
            "Railway",
            "train scheduling"
          ]
        },
        "technique_matches": {
          "AI": [
            "artificial intelligence"
          ],
          "최적화": [
            "Optimization"
          ]
        }
      },
      "id": "auto-arxiv-2609-16939",
      "arxiv_id": "2609.16939",
      "title": "Review on Electric Railway System Optimization: Train Dynamic Scheduling, Energy Management, and Storage Integration",
      "summary_ko": "The modernization of railway systems is being driven by the need for greater efficiency, sustainability, and intelligent operation. In this review, recent advancements in dynamic scheduling, energy management, and energy storage systems (ESSs) integration within electric railway networks are analyzed. Optimization strategies for train scheduling and operation control are reviewed, with a focus on methods that reduce energy consumption and improve overall system performance. The role of energy storage technologies is analyzed in terms of energy management, peak shaving, and voltage and frequency control for practical application. The integration of artificial intelligence and advanced control strategies is also reviewed in electrified railway systems. This review provides a comprehensive overview of current research trends and outlines future directions for the development of resilient, energy efficient, and intelligent railway systems with ESSs integration.",
      "first_seen_at": "2026-10-19T17:04:15Z",
      "discovery_sources": [],
      "authors": [
        "Fei Liu",
        "Can Wan",
        "Stefan Östlund",
        "Qianwen Xu"
      ],
      "published_at": "2026-09-15T10:15:03Z",
      "updated_at": "2026-09-15T10:15:03Z",
      "abstract": "The modernization of railway systems is being driven by the need for greater efficiency, sustainability, and intelligent operation. In this review, recent advancements in dynamic scheduling, energy management, and energy storage systems (ESSs) integration within electric railway networks are analyzed. Optimization strategies for train scheduling and operation control are reviewed, with a focus on methods that reduce energy consumption and improve overall system performance. The role of energy storage technologies is analyzed in terms of energy management, peak shaving, and voltage and frequency control for practical application. The integration of artificial intelligence and advanced control strategies is also reviewed in electrified railway systems. This review provides a comprehensive overview of current research trends and outlines future directions for the development of resilient, energy efficient, and intelligent railway systems with ESSs integration.",
      "source_url": "https://arxiv.org/abs/2609.16939",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-08T09:11:53Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robot",
            "Robots"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2610-09488",
      "arxiv_id": "2610.09488",
      "title": "SiGNgapore - An Interactive Dataset for Sign-based Visual Navigation",
      "summary_ko": "Robots navigating open-world, human-centered environments must look beyond prebuilt high-fidelity maps and leverage navigational aids embedded in their surroundings, particularly, signs. To advance sign-guided visual navigation, we introduce SiGNgapore, an interactive, multimodal dataset designed to benchmark sign-guided multi-step navigational decision-making. SiGNgapore covers diverse public spaces in Singapore, including shopping malls, public transportation hubs, and hospitals, featuring multi-floor, multi-building layouts and indoor-outdoor transitions. It consists of 56 long-horizon navigation missions and 456 scenes, each containing a navigational sign and its surrounding environment. We provide synchronized RGB, depth, IMU, and odometry data for each scene. We additionally provide the layout map and the GPS-aligned scene graph for selected mission sites. Beyond navigational decision-making, SiGNgapore supports a variety of spatial-semantic reasoning tasks, including navigational sign parsing and visual sign grounding, with the ultimate aim of driving progress in mapless robot navigation.",
      "first_seen_at": "2026-10-19T17:13:35Z",
      "discovery_sources": [
        {
          "source_id": "openrobotics-discourse",
          "source_name": "ROS · Open Robotics Discourse",
          "source_url": "https://discourse.openrobotics.org/latest",
          "context_title": "ROS News for the Week of October 12th, 2026",
          "context_summary": "ROS News for the Week of October 12th, 2026 We have a ton of great events lined up for this weekend and next week including: ROSCon China in Shanghai , RoboBusiness in Santa Clara , our monthly Gazebo Community Meeting on scalable multi-robot simulation in ROS 2 and Gazebo , RobOpsCon 26 in Mountain View , our October ROS By-The-Bay Meetup at Intrinsic , and our bi-weekly ROS-ffice Hours . Have you ever wanted to ru…"
        }
      ],
      "authors": [
        "Nicky Zimmerman",
        "Joel Loo",
        "Zishuo Wang",
        "David Hsu"
      ],
      "published_at": "2026-10-07T05:44:03Z",
      "updated_at": "2026-10-14T05:28:12Z",
      "abstract": "Robots navigating open-world, human-centered environments must look beyond prebuilt high-fidelity maps and leverage navigational aids embedded in their surroundings, particularly, signs. To advance sign-guided visual navigation, we introduce SiGNgapore, an interactive, multimodal dataset designed to benchmark sign-guided multi-step navigational decision-making. SiGNgapore covers diverse public spaces in Singapore, including shopping malls, public transportation hubs, and hospitals, featuring multi-floor, multi-building layouts and indoor-outdoor transitions. It consists of 56 long-horizon navigation missions and 456 scenes, each containing a navigational sign and its surrounding environment. We provide synchronized RGB, depth, IMU, and odometry data for each scene. We additionally provide the layout map and the GPS-aligned scene graph for selected mission sites. Beyond navigational decision-making, SiGNgapore supports a variety of spatial-semantic reasoning tasks, including navigational sign parsing and visual sign grounding, with the ultimate aim of driving progress in mapless robot navigation.",
      "source_url": "https://arxiv.org/abs/2610.09488",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "제조"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-08T09:11:53Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "제조": [
            "Manufacturing"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation",
            "simulations"
          ]
        }
      },
      "id": "auto-arxiv-2611-04117",
      "arxiv_id": "2611.04117",
      "title": "Life Cycle Sustainability Assessment of Fossil- and Bio-Based Membrane Manufacturing",
      "summary_ko": "The transition towards sustainable membrane manufacturing is challenged by its reliance on fossil-based polymers, organic solvents, and generation of large volumes of contaminated water. We combined process simulation, life cycle assessment, and Monte Carlo simulation to identify hotspots in the manufacturing of ultrafiltration hollow-fiber membranes for pharmaceutical applications and evaluate strategies to mitigate impact and reduce waste generation. Process models informed by industry input compared fossil- and bio-based membranes on a cradle-to-gate basis per square meter of membrane produced. Key environmental hotspots include polymer and bore solution preparation, driven by the contribution of polymer and solvent, accounting for 30-80% of total impacts across the categories investigated, and wastewater dilution, driven by the environmental burden of solvent-contaminated wastewater management. Under the modeled conditions, although polymer substitution can reduce environmental burdens, cellulose-based membranes show higher impacts than polysulfone membranes in at least 85% of simulations within each impact category, due to chemical-intensive cellulose modification and regeneration. In contrast, solvent recycling can mitigate solvent- and wastewater-related burdens when combined with reduced water demand, yielding climate change benefits at water demands below approximately 36 L per m2 under the modeled recovery configuration and highlighting its potential to reduce the environmental footprint of membrane manufacturing.",
      "first_seen_at": "2026-11-04T09:29:09Z",
      "discovery_sources": [],
      "authors": [
        "Elizabeth Aigaje-Espinosa",
        "Christina Carbrello",
        "Rui Shi"
      ],
      "published_at": "2026-11-03T00:49:48Z",
      "updated_at": "2026-11-03T00:49:48Z",
      "abstract": "The transition towards sustainable membrane manufacturing is challenged by its reliance on fossil-based polymers, organic solvents, and generation of large volumes of contaminated water. We combined process simulation, life cycle assessment, and Monte Carlo simulation to identify hotspots in the manufacturing of ultrafiltration hollow-fiber membranes for pharmaceutical applications and evaluate strategies to mitigate impact and reduce waste generation. Process models informed by industry input compared fossil- and bio-based membranes on a cradle-to-gate basis per square meter of membrane produced. Key environmental hotspots include polymer and bore solution preparation, driven by the contribution of polymer and solvent, accounting for 30-80% of total impacts across the categories investigated, and wastewater dilution, driven by the environmental burden of solvent-contaminated wastewater management. Under the modeled conditions, although polymer substitution can reduce environmental burdens, cellulose-based membranes show higher impacts than polysulfone membranes in at least 85% of simulations within each impact category, due to chemical-intensive cellulose modification and regeneration. In contrast, solvent recycling can mitigate solvent- and wastewater-related burdens when combined with reduced water demand, yielding climate change benefits at water demands below approximately 36 L per m2 under the modeled recovery configuration and highlighting its potential to reduce the environmental footprint of membrane manufacturing.",
      "source_url": "https://arxiv.org/abs/2611.04117",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "우주"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-08T09:11:53Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "우주": [
            "Satellite",
            "satellites"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "Simulation"
          ]
        }
      },
      "id": "auto-arxiv-2611-08174",
      "arxiv_id": "2611.08174",
      "title": "NOMA Assisted Coordinated Beam Hopping for VLEO Satellite Communication Systems",
      "summary_ko": "This paper investigates a non-orthogonal multiple access assisted coordinated beam hopping (NOMA-CBH) scheme in multidimensional very low Earth orbit (VLEO) satellite communication systems, where multiple satellites employ CBH and coordinated beams jointly serve ground users and a dedicated ground station (GS). To characterize the VLEO-specific propagation environment, we develop a channel model that incorporates atmospheric attenuation, Doppler effects, and pointing perturbations. Building upon this architecture and channel model, a low-complexity decoupled (LCD) algorithm is designed to optimize beam scheduling and power allocation. We further derive closed-form outage probability expressions for the ground users and the GS, perform asymptotic analysis to characterize their diversity orders, and evaluate the delay-limited throughput. Simulation results demonstrate that: i) The NOMA-CBH with LCD algorithm achieves an enhanced system sum rate relative to orthogonal multiple access with coordinated beam hopping (OMA-CBH); ii) Across the considered heterogeneous traffic-demand operating points, the NOMA-CBH scheme achieves higher aggregate delivered traffic than the benchmark schemes; and iii) The outage performance of NOMA-CBH is better than that of OMA-CBH.",
      "doi": "10.1109/TAES.2026.3750099",
      "first_seen_at": "2026-11-06T09:31:54Z",
      "discovery_sources": [],
      "authors": [
        "Jiayang Kang",
        "Xinwei Yue",
        "Xuyang Zhang",
        "Tian Li",
        "Peng Yang",
        "Chunyu Pan",
        "Shaoli Kang"
      ],
      "published_at": "2026-11-05T15:31:08Z",
      "updated_at": "2026-11-05T15:31:08Z",
      "abstract": "This paper investigates a non-orthogonal multiple access assisted coordinated beam hopping (NOMA-CBH) scheme in multidimensional very low Earth orbit (VLEO) satellite communication systems, where multiple satellites employ CBH and coordinated beams jointly serve ground users and a dedicated ground station (GS). To characterize the VLEO-specific propagation environment, we develop a channel model that incorporates atmospheric attenuation, Doppler effects, and pointing perturbations. Building upon this architecture and channel model, a low-complexity decoupled (LCD) algorithm is designed to optimize beam scheduling and power allocation. We further derive closed-form outage probability expressions for the ground users and the GS, perform asymptotic analysis to characterize their diversity orders, and evaluate the delay-limited throughput. Simulation results demonstrate that: i) The NOMA-CBH with LCD algorithm achieves an enhanced system sum rate relative to orthogonal multiple access with coordinated beam hopping (OMA-CBH); ii) Across the considered heterogeneous traffic-demand operating points, the NOMA-CBH scheme achieves higher aggregate delivered traffic than the benchmark schemes; and iii) The outage performance of NOMA-CBH is better than that of OMA-CBH.",
      "source_url": "https://arxiv.org/abs/2611.08174",
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "시뮬레이션",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-08T09:11:53Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robot",
            "robots",
            "robotic"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation",
            "simulator"
          ],
          "최적화": [
            "optimisation"
          ]
        }
      },
      "id": "auto-arxiv-2611-03216",
      "arxiv_id": "2611.03216",
      "title": "Towards end-to-end co-design for one-shot printable task-specific soft robots",
      "summary_ko": "Soft robot co-design methods can jointly optimise morphology and control, but the resulting designs are often difficult to manufacture, while manufacturable approaches typically restrict actuator placement or require manual assembly. We present a simulation-based co-design framework that jointly optimises material distribution, pneumatic actuator placement and orientation, and control, while producing designs that can be fabricated monolithically using fused deposition modelling. The framework uses a differentiable 2D plane-stress Material Point Method simulator, incorporating an experimentally validated material model and an empirically derived surrogate model of a 3D-printed inflatable actuator. We demonstrate gradient-based optimisation soft robotic arms for prescribed tip trajectories. Optimised designs are transferred directly from simulation to one-shot monolithic FDM fabrication, with integrated actuators and no post-assembly. Physical experiments demonstrate that the resulting arms reproduce the behaviour predicted in simulation. This framework provides a path from task-level objectives to physically realisable soft robots while retaining substantial freedom over both morphology and actuation.",
      "first_seen_at": "2026-11-03T09:42:47Z",
      "discovery_sources": [],
      "authors": [
        "James Wood",
        "André Farinha",
        "Will Browne",
        "Josh Pinskier",
        "Krishna Manaswi Digumarti"
      ],
      "published_at": "2026-11-02T14:39:49Z",
      "updated_at": "2026-11-02T14:39:49Z",
      "abstract": "Soft robot co-design methods can jointly optimise morphology and control, but the resulting designs are often difficult to manufacture, while manufacturable approaches typically restrict actuator placement or require manual assembly. We present a simulation-based co-design framework that jointly optimises material distribution, pneumatic actuator placement and orientation, and control, while producing designs that can be fabricated monolithically using fused deposition modelling. The framework uses a differentiable 2D plane-stress Material Point Method simulator, incorporating an experimentally validated material model and an empirically derived surrogate model of a 3D-printed inflatable actuator. We demonstrate gradient-based optimisation soft robotic arms for prescribed tip trajectories. Optimised designs are transferred directly from simulation to one-shot monolithic FDM fabrication, with integrated actuators and no post-assembly. Physical experiments demonstrate that the resulting arms reproduce the behaviour predicted in simulation. This framework provides a path from task-level objectives to physically realisable soft robots while retaining substantial freedom over both morphology and actuation.",
      "source_url": "https://arxiv.org/abs/2611.03216",
      "doi": null,
      "observed_at": "2026-11-09T16:05:22Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T16:05:22Z",
      "refresh_status": "refreshed"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-09T09:56:32Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "robotic"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "Simulation"
          ]
        }
      },
      "id": "auto-arxiv-2610-25967",
      "arxiv_id": "2610.25967",
      "title": "Reconstruction and Discrete-Event Simulation Assessment of an Analytical",
      "summary_ko": "This article presents an analytical-DES benchmark for intra-aisle robotic picking in automated warehousing. The investigated cycle combines horizontal shuttle travel, tote handling and item picking at storage locations. Simulation results are compared with the analytical formulation of Lerher et al. under the same rack geometry, shuttle-motion, handling and picking conditions, and the same definitions of cycle time and derived item throughput. Reproducing the published analytical tables requires the distance interpretation dES = 1.5 m and dTB = 0 m. The analytical-DES benchmark comprises 180 scenarios formed from three rack lengths, two paired motion profiles and n = 1 to 30 visited picking locations, with ten simulation replications per scenario. The six n = 1 cases are reported separately because no travel-between component is present. Across the remaining 174 scenarios, the mean absolute relative cycle-time discrepancy is 0.8655%, the median is 0.6758%, and the maximum is 7.0370%. The mean absolute error is 2.411 s and the RMSE is 2.744 s. The simulated scenario-mean cycle time is below the analytical value in all 174 principal scenarios. The results therefore show close numerical correspondence under the tested conditions, but not exact agreement, formal statistical equivalence, or validation against an operating warehouse.",
      "first_seen_at": "2026-10-20T07:44:48Z",
      "discovery_sources": [],
      "authors": [
        "Mohsen Reda Mahmoud Moursi",
        "Ahmed Stohy"
      ],
      "published_at": "2026-10-19T08:00:21Z",
      "updated_at": "2026-10-19T08:00:21Z",
      "abstract": "This article presents an analytical-DES benchmark for intra-aisle robotic picking in automated warehousing. The investigated cycle combines horizontal shuttle travel, tote handling and item picking at storage locations. Simulation results are compared with the analytical formulation of Lerher et al. under the same rack geometry, shuttle-motion, handling and picking conditions, and the same definitions of cycle time and derived item throughput. Reproducing the published analytical tables requires the distance interpretation dES = 1.5 m and dTB = 0 m. The analytical-DES benchmark comprises 180 scenarios formed from three rack lengths, two paired motion profiles and n = 1 to 30 visited picking locations, with ten simulation replications per scenario. The six n = 1 cases are reported separately because no travel-between component is present. Across the remaining 174 scenarios, the mean absolute relative cycle-time discrepancy is 0.8655%, the median is 0.6758%, and the maximum is 7.0370%. The mean absolute error is 2.411 s and the RMSE is 2.744 s. The simulated scenario-mean cycle time is below the analytical value in all 174 principal scenarios. The results therefore show close numerical correspondence under the tested conditions, but not exact agreement, formal statistical equivalence, or validation against an operating warehouse.",
      "source_url": "https://arxiv.org/abs/2610.25967",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-09T09:56:32Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power system",
            "power systems",
            "renewable energy"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2611-02696",
      "arxiv_id": "2611.02696",
      "title": "Protection challenges and solutions on power systems towards 100% inverter-based resources and energy storage systems",
      "summary_ko": "The decarbonization of the energy sector, driven by the Paris Agreement and the European Green Deal, is increasing the share of renewable energy sources (RES) in power systems. Unlike traditional systems, RES-based systems rely on power electronic converters, which creates unprecedented challenges for reliability and resilience. The future power system is expected to combine a low-level AC layer with an upper-level High Voltage Direct Current (HVDC) backbone, which requires new understanding of fault protection, stability, and interaction between AC and DC components, as well as new models and simulation tools. This is especially critical in systems moving towards 100 percent converter-based generation. Protection systems detect, locate, and isolate faults to protect assets while the rest of the system keeps operating. Traditionally, they rely on the large fault currents supplied by synchronous machines. In modern systems, converter controls limit the fault current contribution to about 120 percent of rated current, so relays that distinguish faults from normal overcurrents based on current level may operate incorrectly. In addition, inverter-based resources (IBR) may not inject enough negative and zero sequence current for directional relays to work properly, and converters hold the voltage at a fixed value only within their reach, beyond which it drops quickly. As a result, existing protection schemes may not provide the expected accuracy and reliability. This report reviews the protection challenges identified in recent academic literature for power systems with high shares of converter-based resources. It covers the specific characteristics of RES, the challenges for protection with intelligent electronic devices (IED), the challenges of converter control during faults, and the main solutions proposed in research over the last decade.",
      "doi": "10.5281/zenodo.19736793",
      "first_seen_at": "2026-11-03T09:42:47Z",
      "discovery_sources": [],
      "authors": [
        "Alexander Novikov",
        "Luis A. Garcia-Reyes",
        "Anup Joshi",
        "Ahmar Farooque",
        "Junaid Khalid",
        "Dirk Van Hertem"
      ],
      "published_at": "2026-11-02T07:47:29Z",
      "updated_at": "2026-11-02T07:47:29Z",
      "abstract": "The decarbonization of the energy sector, driven by the Paris Agreement and the European Green Deal, is increasing the share of renewable energy sources (RES) in power systems. Unlike traditional systems, RES-based systems rely on power electronic converters, which creates unprecedented challenges for reliability and resilience. The future power system is expected to combine a low-level AC layer with an upper-level High Voltage Direct Current (HVDC) backbone, which requires new understanding of fault protection, stability, and interaction between AC and DC components, as well as new models and simulation tools. This is especially critical in systems moving towards 100 percent converter-based generation. Protection systems detect, locate, and isolate faults to protect assets while the rest of the system keeps operating. Traditionally, they rely on the large fault currents supplied by synchronous machines. In modern systems, converter controls limit the fault current contribution to about 120 percent of rated current, so relays that distinguish faults from normal overcurrents based on current level may operate incorrectly. In addition, inverter-based resources (IBR) may not inject enough negative and zero sequence current for directional relays to work properly, and converters hold the voltage at a fixed value only within their reach, beyond which it drops quickly. As a result, existing protection schemes may not provide the expected accuracy and reliability. This report reviews the protection challenges identified in recent academic literature for power systems with high shares of converter-based resources. It covers the specific characteristics of RES, the challenges for protection with intelligent electronic devices (IED), the challenges of converter control during faults, and the main solutions proposed in research over the last decade.",
      "source_url": "https://arxiv.org/abs/2611.02696",
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "제조"
      ],
      "techniques": [
        "시뮬레이션"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-09T09:56:32Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "제조": [
            "Manufacturing"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ]
        }
      },
      "id": "auto-arxiv-2611-05758",
      "arxiv_id": "2611.05758",
      "title": "Mechanical Engineering Skills: An Open Library of Agent Skills for Design, Analysis and Manufacturing",
      "summary_ko": "Mechanical engineers use specialized tools for CAD, simulation, manufacturing and research, each with a distinct interface and learning curve. We present Mechanical Engineering Skills, an open library of 33 agent skills that makes these capabilities accessible through a single text or voice conversation. Each skill combines operating instructions, executable tools and verification procedures. A language model interprets the request, selects and coordinates skills, while deterministic tools perform computations and return checked results. Default workflows require no tool-specific interface training, local model execution or model fine-tuning; skills load on demand and install with one command. Nineteen skills adapt published or preprint research from our laboratory, including text-to-CAD, linkage synthesis, CFD setup, stress-field and lattice surrogates, equation discovery, additive-manufacturing planning and in-process monitoring. Fourteen additional skills support core engineering and education. We describe the skill format, design principles and a method for translating research into practical workflows. Five case studies with explicit evidence scopes and independent reruns demonstrate exact analytical agreement for lattice homogenization, rocket apogee predictions within 4% of stored OpenRocket references, an equation fit nine times more accurate than a cubic polynomial at an unseen temperature, and an end-to-end rehearsal of porosity monitoring. Controlled agent comparisons remain future work. The open library is available at https://github.com/BaratiLab/mechanical-engineering-skills.",
      "first_seen_at": "2026-11-05T09:38:48Z",
      "discovery_sources": [],
      "authors": [
        "Achuth Chandrasekhar",
        "Jesse Barkley",
        "Peter Pak",
        "Rumi Loghmani",
        "Aakash Oza",
        "Amir Barati Farimani"
      ],
      "published_at": "2026-11-03T23:57:51Z",
      "updated_at": "2026-11-03T23:57:51Z",
      "abstract": "Mechanical engineers use specialized tools for CAD, simulation, manufacturing and research, each with a distinct interface and learning curve. We present Mechanical Engineering Skills, an open library of 33 agent skills that makes these capabilities accessible through a single text or voice conversation. Each skill combines operating instructions, executable tools and verification procedures. A language model interprets the request, selects and coordinates skills, while deterministic tools perform computations and return checked results. Default workflows require no tool-specific interface training, local model execution or model fine-tuning; skills load on demand and install with one command. Nineteen skills adapt published or preprint research from our laboratory, including text-to-CAD, linkage synthesis, CFD setup, stress-field and lattice surrogates, equation discovery, additive-manufacturing planning and in-process monitoring. Fourteen additional skills support core engineering and education. We describe the skill format, design principles and a method for translating research into practical workflows. Five case studies with explicit evidence scopes and independent reruns demonstrate exact analytical agreement for lattice homogenization, rocket apogee predictions within 4% of stored OpenRocket references, an equation fit nine times more accurate than a cubic polynomial at an unseen temperature, and an end-to-end rehearsal of porosity monitoring. Controlled agent comparisons remain future work. The open library is available at https://github.com/BaratiLab/mechanical-engineering-skills.",
      "source_url": "https://arxiv.org/abs/2611.05758",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "로봇"
      ],
      "techniques": [
        "AI",
        "시뮬레이션",
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-09T09:56:32Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "로봇": [
            "Robot"
          ]
        },
        "technique_matches": {
          "AI": [
            "reinforcement learning"
          ],
          "시뮬레이션": [
            "simulation"
          ],
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2610-25946",
      "arxiv_id": "2610.25946",
      "title": "Policy Search in Code Space for Multi-Agent Pathfinding",
      "summary_ko": "Developing coordination policies for complex multi-agent systems relies on either labor-intensive algorithm design or opaque task-specific reinforcement learning. With recent advances in autonomous coding agents, we show that policy search directly in code space offers a compelling third path that combines automated optimization with explicit interpretability. We investigate a human-directed workflow in which coding agents iteratively propose, implement, evaluate, diagnose, and eventually refine algorithms guided by simulation feedback and explicit correctness verification. We conducted two case studies and found that this workflow robustly outperformed any manually engineered solutions. On a challenging map in the 2024 League of Robot Runners competition, a two-day search discovered a policy that exceeds the stronger locally reproduced competition entry by 35.0% on the densest instance and generalizes to unseen map instances. In simulated real-world warehouses with irregular lanes, continuous robot geometry, and asynchronous motion, a week-long search discovered an entirely new class of multi-agent pathfinding algorithm that outperforms a calibrated proprietary solution in throughput by 58.9%. Under stated assumptions, we further prove that the new algorithm is collision-free and completes, in finite time, every accepted goal that is not withdrawn or replaced. Together, the studies show that policy search in code space powered by state-of-the-art coding agents offers a highly competitive path to building real engineering solutions that are both effective and analytically tractable.",
      "first_seen_at": "2026-10-20T07:44:48Z",
      "discovery_sources": [],
      "authors": [
        "Matthias Eder",
        "Pantelis Emmanouil",
        "Vivek Farias",
        "Fabian Fischer",
        "Adam Jozefiak",
        "Tianyi Peng",
        "Thomas Ulz",
        "Raphael Watschinger",
        "Fangyu Wu",
        "Andrew Zheng"
      ],
      "published_at": "2026-10-19T07:39:25Z",
      "updated_at": "2026-10-20T03:42:05Z",
      "abstract": "Developing coordination policies for complex multi-agent systems relies on either labor-intensive algorithm design or opaque task-specific reinforcement learning. With recent advances in autonomous coding agents, we show that policy search directly in code space offers a compelling third path that combines automated optimization with explicit interpretability. We investigate a human-directed workflow in which coding agents iteratively propose, implement, evaluate, diagnose, and eventually refine algorithms guided by simulation feedback and explicit correctness verification. We conducted two case studies and found that this workflow robustly outperformed any manually engineered solutions. On a challenging map in the 2024 League of Robot Runners competition, a two-day search discovered a policy that exceeds the stronger locally reproduced competition entry by 35.0% on the densest instance and generalizes to unseen map instances. In simulated real-world warehouses with irregular lanes, continuous robot geometry, and asynchronous motion, a week-long search discovered an entirely new class of multi-agent pathfinding algorithm that outperforms a calibrated proprietary solution in throughput by 58.9%. Under stated assumptions, we further prove that the new algorithm is collision-free and completes, in finite time, every accepted goal that is not withdrawn or replaced. Together, the studies show that policy search in code space powered by state-of-the-art coding agents offers a highly competitive path to building real engineering solutions that are both effective and analytically tractable.",
      "source_url": "https://arxiv.org/abs/2610.25946",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "시뮬레이션",
        "최적화",
        "데이터 분석"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-09T09:56:32Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power systems"
          ]
        },
        "technique_matches": {
          "시뮬레이션": [
            "simulation"
          ],
          "최적화": [
            "optimization"
          ],
          "데이터 분석": [
            "Time Series",
            "time-series"
          ]
        }
      },
      "id": "auto-arxiv-2611-01425",
      "arxiv_id": "2611.01425",
      "title": "Uncertainty-Aware Joint Generative Modeling of Fault Event Sequences and Sensor Time Series in Solar PV Plants",
      "summary_ko": "This paper presents a multimodal generative model designed to simulate a solar energy plant system for predictive maintenance and fault prediction. We propose a novel approach to enhance system generation through conditional generation by leveraging both event sequences and time-series data and improve next event prediction. The framework employs a Transformer Hawkes Process (THP) to encode event data and an iTransformer to encode time-series data, integrated via a co-attention mechanism that effectively captures cross-modal dependencies between discrete events and continuous signals. From these integrated representations, we employ two specialized decoders for simulation: a Conditional Latent Diffusion Model (CLDM) for modeling future sensor trajectories and the Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process (SMURF-THP) for high-fidelity event prediction. This dual-decoder strategy supports uncertainty-aware, history-conditioned simulation by rolling the model forward for a specified number of generation iterations, producing finite stochastic scenarios rather than deterministic extrapolations over the full observational record. Evaluated on a comprehensive dataset from the Red Rock Solar Site in Arizona, our approach improves aggregate event-prediction F1 relative to baselines and supports applications in predictive maintenance, fault detection, and energy optimization for solar power systems.",
      "first_seen_at": "2026-11-03T09:42:47Z",
      "discovery_sources": [],
      "authors": [
        "Jiayu Huang",
        "Boyang Xu",
        "Yongming Liu",
        "Hao Yan"
      ],
      "published_at": "2026-11-01T02:07:45Z",
      "updated_at": "2026-11-01T02:07:45Z",
      "abstract": "This paper presents a multimodal generative model designed to simulate a solar energy plant system for predictive maintenance and fault prediction. We propose a novel approach to enhance system generation through conditional generation by leveraging both event sequences and time-series data and improve next event prediction. The framework employs a Transformer Hawkes Process (THP) to encode event data and an iTransformer to encode time-series data, integrated via a co-attention mechanism that effectively captures cross-modal dependencies between discrete events and continuous signals. From these integrated representations, we employ two specialized decoders for simulation: a Conditional Latent Diffusion Model (CLDM) for modeling future sensor trajectories and the Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process (SMURF-THP) for high-fidelity event prediction. This dual-decoder strategy supports uncertainty-aware, history-conditioned simulation by rolling the model forward for a specified number of generation iterations, producing finite stochastic scenarios rather than deterministic extrapolations over the full observational record. Evaluated on a comprehensive dataset from the Red Rock Solar Site in Arizona, our approach improves aggregate event-prediction F1 relative to baselines and supports applications in predictive maintenance, fault detection, and energy optimization for solar power systems.",
      "source_url": "https://arxiv.org/abs/2611.01425",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    },
    {
      "fields": [
        "에너지"
      ],
      "techniques": [
        "최적화"
      ],
      "tags": [],
      "design_note": "",
      "operations_note": "",
      "origin": "auto",
      "added_at": "2026-11-09T09:56:32Z",
      "classification": {
        "method": "keyword-rules-v1",
        "review_status": "unreviewed",
        "field_matches": {
          "에너지": [
            "power system"
          ]
        },
        "technique_matches": {
          "최적화": [
            "optimization"
          ]
        }
      },
      "id": "auto-arxiv-2611-04896",
      "arxiv_id": "2611.04896",
      "title": "On the Convexity of Common Resource Adequacy and Capacity Accreditation Metrics",
      "summary_ko": "Many emerging capacity expansion optimization formulations implicitly presume the convexity (or concavity) of power system adequacy risk (or capacity accreditation) metrics. These assumptions have yet to be formally justified. We present concise mathematical proofs validating the claim for certain metrics (expected unserved energy and effective load carrying capability), but also provide simple invalidating counterexamples for others (loss-of-load expectation and equivalent firm capacity). Formulations built on non-convex (non-concave) metrics structurally undervalue candidate portfolio adequacy, and so cannot claim to produce least-cost expansion plans.",
      "first_seen_at": "2026-11-04T09:29:09Z",
      "discovery_sources": [],
      "authors": [
        "Gord Stephen"
      ],
      "published_at": "2026-11-03T13:43:18Z",
      "updated_at": "2026-11-03T13:43:18Z",
      "abstract": "Many emerging capacity expansion optimization formulations implicitly presume the convexity (or concavity) of power system adequacy risk (or capacity accreditation) metrics. These assumptions have yet to be formally justified. We present concise mathematical proofs validating the claim for certain metrics (expected unserved energy and effective load carrying capability), but also provide simple invalidating counterexamples for others (loss-of-load expectation and equivalent firm capacity). Formulations built on non-convex (non-concave) metrics structurally undervalue candidate portfolio adequacy, and so cannot claim to produce least-cost expansion plans.",
      "source_url": "https://arxiv.org/abs/2611.04896",
      "doi": null,
      "observed_at": "2026-11-09T09:57:54Z",
      "source_status": "ok",
      "attempted_at": "2026-11-09T09:57:54Z",
      "refresh_status": "deferred"
    }
  ],
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}