Agentic Interpretation: Lattice-Structured Evidence for LLM-Based Program Analysis
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 judgments into a single opaque answer, rather than exposing which conclusions are supported or disputed and using intermediate findings to guide later, more focused searches. In this paper, we propose ...
| Authors | Jacqueline L. Mitchell, Chao Wang |
|---|---|
| Primary category | cs.SE |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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