{
  "id": 881,
  "url": "https://arxiv.org/abs/2606.18037v1",
  "title": "ProvenanceGuard: Source-Aware Factuality Verification for MCP-Based LLM Agents",
  "summary": "Tool-using LLM agents increasingly use the Model Context Protocol (MCP) to answer from heterogeneous evidence sources, including search, APIs, databases, clinical records, and formulary tools. Standard factuality metrics usually test whether an answer is supported by pooled evidence, missing a provenance-sensitive failure mode: a claim may be supported somewhere while being attributed to the wrong source. We call this cross-source conflation. We introduce ProvenanceGuard, a source-aware verifier",
  "authors": "Ander Alvarez, Santhiya Rajan, Samuel Mugel, Román Orús",
  "category": "research",
  "topics": "healthcare,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-16T15:10:29.000Z",
  "fetched_at": "2026-07-14T14:14:50.327Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/881",
  "original_url": "https://arxiv.org/abs/2606.18037v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}