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Human-Supervised Artificial Intelligence Researchers in Electric Power Systems: Knowledge, Physics, Simulation, and Governance for Large Language Models — arXiv:2611.02711

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02711
License (as archived): CC0-1.0
Archived: 2026-11-03T11:03:18.929Z
SHA-256 of the archived text: 4b2bd23cbed46bc0531e3d783083506adb34ec5a4a3e647419c59edc861ed248
Stable URL of this page: https://metraipolis.com/documento/dc_9dfedd5cb668

How to cite

Human-Supervised Artificial Intelligence Researchers in Electric Power Systems: Knowledge, Physics, Simulation, and Governance for Large Language Models — arXiv:2611.02711 — arXiv (en), https://arxiv.org/abs/2611.02711 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_9dfedd5cb668 (Metraipolis archive dc_9dfedd5cb668, sha256 4b2bd23cbed46bc0531e3d783083506adb34ec5a4a3e647419c59edc861ed248)

BibTeX

@misc{dc9dfedd5cb668,
  title = {Human-Supervised Artificial Intelligence Researchers in Electric Power Systems: Knowledge, Physics, Simulation, and Governance for Large Language Models — arXiv:2611.02711},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02711},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_9dfedd5cb668; sha256 4b2bd23cbed46bc0531e3d783083506adb34ec5a4a3e647419c59edc861ed248},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02711. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.02711 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:59:45.845Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).