Knowledge Boundary Probing and Demand-Guided Intervention for LLM-Based Power System Code Generation
arXiv:2605.31478v1 Announce Type: cross Abstract: 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 li
The increasing reliance on LLMs for critical infrastructure automation, especially in power systems, necessitates addressing their reliability and deployment challenges.
This research highlights a critical hurdle for deploying AI in sensitive sectors: the need for reliable, auditable, and locally-served models due to confidentiality and regulatory concerns.
The focus shifts from general LLM reasoning to specific 'structured API-knowledge boundary errors' as the key challenge for practical, on-premise AI power system automation.
- · Open-weight LLM developers
- · On-premise AI solution providers
- · Energy utilities prioritizing data privacy
- · General-purpose, cloud-only LLM vendors
- · Developers neglecting domain-specific API knowledge
Increased emphasis on fine-tuning LLMs with highly specialized domain knowledge and API specifications for critical infrastructure applications.
Development of new MLOps frameworks tailored for secure, on-premise deployment and continuous validation of AI models in regulated industries.
Potential for a bifurcated AI market where highly specialized, secure, and auditable models dominate critical infrastructure sectors, distinct from general-purpose cloud AI.
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Read at arXiv cs.CL