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This research introduces a rigorous auditing framework for multimodal LLMs used in power grid diagnosis, ensuring models rely on appropriate evidence rather than just generating accurate answers. By comparing self-reported reliance with behavioral interventions under modality ablations, the study validates mechanisms to detect and correct faithfulness failures without sacrificing performance. The findings are critical for deploying trustworthy AI systems in high-stakes infrastructure monitoring where grounding is as important as prediction accuracy.
Task-Conditional Faithfulness Auditing of Multimodal LLMs for Grid Diagnosis
arXiv:2607.24539v1 Announce Type: new Abstract: Multimodal large language models (LLMs) can combine topology, measurements, and incident text for grid diagnosis, yet answer accuracy does not establish that task-appropriate evidence was used. This letter proposes a general framework in order to conduct task-conditional faithfulness audit. It compares self-reported reliance, intervention-derived behavioral reliance, and preregistered engineering importance. The framework first registers task-specific evidence requirements and compares them with self-reported reliance and behavioral changes under controlled modality ablations. To resolve detected discrepancies, we design an evidence-gated correction and re-audit mechanism that regenerates failed responses under evidence constraints and independently re-ablates them to verify improved grounding without performance loss. Case studies evaluate three differently scaled LLMs on IEEE 39- and 118-bus scenarios. These results validate the framework ability to detect, diagnose, and correct task-conditional faithfulness failures.
Abstract:Multimodal large language models (LLMs) can combine topology, measurements, and incident text for grid diagnosis, yet answer accuracy does not establish that task-appropriate evidence was used. This letter proposes a general framework in order to conduct task-conditional faithfulness audit. It compares self-reported reliance, intervention-derived behavioral reliance, and preregistered engineering importance. The framework first registers task-specific evidence requirements and compares them with self-reported reliance and behavioral changes under controlled modality ablations. To resolve detected discrepancies, we design an evidence-gated correction and re-audit mechanism that regenerates failed responses under evidence constraints and independently re-ablates them to verify improved grounding without performance loss. Case studies evaluate three differently scaled LLMs on IEEE 39- and 118-bus scenarios. These results validate the framework ability to detect, diagnose, and correct task-conditional faithfulness failures.
Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
arXiv:2607.24539 [cs.AI]
(or arXiv:2607.24539v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.24539 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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