AI RESEARCH
Knowledge Boundary Probing and Demand-Guided Intervention for LLM-Based Power System Code Generation
arXiv CS.CL
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ArXi:2605.31478v1 Announce Type: cross Large language models (LLMs) are increasingly used to automate power-system analysis, but many utilities and energy-research labs require on-premise serving for confidentiality, regulatory, reproducibility, and cost reasons. This makes the reliability of open-weight models a deployment issue. We show that first-pass failures in power-system code generation are dominated not by reasoning alone, but by structured API-knowledge boundary errors: hallucinated function names, misused parameters, and mishandled result tables in versioned simulation libraries.