Task-Conditional Faithfulness Auditing of Multimodal LLMs for Grid Diagnosis

Tianqiao Zhao; Meng Yue; Jianhui Wang

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arXiv · 2607.24539

Task-Conditional Faithfulness Auditing of Multimodal LLMs for Grid Diagnosis

Tianqiao Zhao·Meng Yue·Jianhui Wang

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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.

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Tianqiao Zhao, Meng Yue, Jianhui Wang. 2026-07-27. Task-Conditional Faithfulness Auditing of Multimodal LLMs for Grid Diagnosis. https://arxiv.org/abs/2607.24539

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