{
  "id": 4437,
  "url": "https://arxiv.org/abs/2605.12694v1",
  "title": "Agentic Interpretation: Lattice-Structured Evidence for LLM-Based Program Analysis",
  "summary": "Large language models can consult information that fixed static analyzers cannot, such as documentation, current security advisories, version-specific metadata, and informal API contracts. This makes LLMs a compelling option for program analyses that depend on information beyond the source program, or that are otherwise not amenable to conventional static analyzers. However, directly asking an LLM for a one-shot whole-program analysis is brittle because it compresses many evidence-dependent judg",
  "authors": "Jacqueline L. Mitchell, Chao Wang",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-12T19:46:24.000Z",
  "fetched_at": "2026-07-14T16:30:59.238Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4437",
  "original_url": "https://arxiv.org/abs/2605.12694v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}