# Agentic Interpretation: Lattice-Structured Evidence for LLM-Based Program Analysis

arXiv: https://arxiv.org/abs/2605.12694 · HTML: https://mlsanity.com/paper/2605.12694/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 25 of its 48 references that are in the map.
- First posted on arXiv 12 May 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 25 of 25)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.04388/) (2023)
- [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://mlsanity.com/paper/2308.09687/) (2023)
- [On the Self-Verification Limitations of Large Language Models on Reasoning and Planning Tasks](https://mlsanity.com/paper/2402.08115/) (2024)
- [Context Length Alone Hurts LLM Performance Despite Perfect Retrieval](https://mlsanity.com/paper/2510.05381/) (2025)
- [Baldur: Whole-Proof Generation and Repair with Large Language Models](https://mlsanity.com/paper/2303.04910/) (2023)
- [IRIS: LLM-Assisted Static Analysis for Detecting Security Vulnerabilities](https://mlsanity.com/paper/2405.17238/) (2024)
- [Can Large Language Models Transform Natural Language Intent into Formal Method Postconditions?](https://mlsanity.com/paper/2310.01831/) (2023)
- [Lemur: Integrating Large Language Models in Automated Program Verification](https://mlsanity.com/paper/2310.04870/) (2023)
- [Measuring Chain of Thought Faithfulness by Unlearning Reasoning Steps](https://mlsanity.com/paper/2502.14829/) (2025)
- [Anchoring Bias in Large Language Models: An Experimental Study](https://mlsanity.com/paper/2412.06593/) (2024)
- [Automated Proof Generation for Rust Code via Self-Evolution](https://mlsanity.com/paper/2410.15756/) (2024)
- [Towards Better Chain-of-Thought: A Reflection on Effectiveness and Faithfulness](https://mlsanity.com/paper/2405.18915/) (2024)
- [Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs](https://mlsanity.com/paper/2410.14641/) (2024)
- [E&V: Prompting Large Language Models to Perform Static Analysis by Pseudo-code Execution and Verification](https://mlsanity.com/paper/2312.08477/) (2023)
- [Beyond Postconditions: Can Large Language Models infer Formal Contracts for Automatic Software Verification?](https://mlsanity.com/paper/2510.12702/) (2025)
- [Sound and Complete Neurosymbolic Reasoning with LLM-Grounded Interpretations](https://mlsanity.com/paper/2507.09751/) (2025)
- [Quokka: Accelerating Program Verification with LLMs via Invariant Synthesis](https://mlsanity.com/paper/2509.21629/) (2025)
- [Agentic Code Reasoning](https://mlsanity.com/paper/2603.01896/) (2026)
- [Anchors in the Machine: Behavioral and Attributional Evidence of Anchoring Bias in LLMs](https://mlsanity.com/paper/2511.05766/) (2025)
- [NESA: Relational Neuro-Symbolic Static Program Analysis](https://mlsanity.com/paper/2412.14399/) (2024)
- [Preguss: It Analyzes, It Specifies, It Verifies](https://mlsanity.com/paper/2508.14532/) (2025)
- [Current Practices for Building LLM-Powered Reasoning Tools Are Ad Hoc -- and We Can Do Better](https://mlsanity.com/paper/2507.05886/) (2025)
- [ZeroFalse: Improving Precision in Static Analysis with LLMs](https://mlsanity.com/paper/2510.02534/) (2025)

## Abstract (arXiv)

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 agentic interpretation, a framework that brings the discipline of lattice-based static analysis to LLM-driven program reasoning. At a high level, agentic interpretation decomposes a high-level analysis goal into localized claims, and tracks the LLM's judgment about each claim in a finite-height lattice. A worklist algorithm governs how claims and their judgments evolve during the analysis. We introduce a formal model of agentic interpretation, explore the design space it opens, and illustrate the approach with a worked example analyzing code that depends on opaque third-party components.