{
  "id": 5947,
  "url": "https://arxiv.org/abs/2604.11322v1",
  "title": "Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations",
  "summary": "Large language models (LLMs) have demonstrated impressive capabilities in utilizing external tools. In practice, however, LLMs are often exposed to tools that are irrelevant to the user's query, in which case the desired behavior is to refrain from invocations. In this work, we identify a widespread yet overlooked mechanistic flaw in tool refusal, which we term structural alignment bias: Even when a tool fails to serve the user's goal, LLMs still tend to invoke it whenever query attributes can b",
  "authors": "Yilong Liu, Xixun Lin, Pengfei Cao, Ge Zhang, Fang Fang, Yanan Cao",
  "category": "research",
  "topics": "bias-fairness,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-13T11:23:36.000Z",
  "fetched_at": "2026-07-14T16:32:06.470Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5947",
  "original_url": "https://arxiv.org/abs/2604.11322v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}