Human-Supervised Artificial Intelligence Researchers in Electric Power Systems: Knowledge, Physics, Simulation, and Governance for Large Language Models
- Published
- Source
- arXiv
- Paper number
- 1425
- Field
- Reasoning / Survey
- arXiv ID
- 2611.02711
Key points
- The authors review power-system LLM applications, related scientific workflows, and physics-grounded power AI studies.
- 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 so that claims can be traced to sources, computations reproduced, and failures and approvals examined.
- On this basis, a staged development roadmap and requirements for research reporting are proposed.
- Evaluation should focus on research artifacts and their validation.
Paper links
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