{
  "id": 14044,
  "url": "https://arxiv.org/abs/2607.24539v1",
  "title": "Task-Conditional Faithfulness Auditing of Multimodal LLMs for Grid Diagnosis",
  "summary": "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",
  "authors": "Tianqiao Zhao, Meng Yue, Jianhui Wang",
  "category": "research",
  "topics": "healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-27T15:17:15.000Z",
  "fetched_at": "2026-07-28T05:10:12.325Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14044",
  "original_url": "https://arxiv.org/abs/2607.24539v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}