Schwarz: Solver-Aware Agentic Program Verification

2026-08-31

AI Summary

本文提出Schwarz系统，通过局部修复任务和求解器友好策略解决程序验证中SMT证明失败的问题，提高验证成功率。

Abstract

Agentic verification systems can often generate source-level specifications that look plausible, but plausibility is not enough: the verifier must still turn those specifications into SMT obligations that the solver can prove. When this step fails, current LLM-driven loops usually expose only a coarse verifier error, timeout, or unknown solver result. The model cannot tell whether the specification is wrong, a helper lemma is missing, the proof context contains irrelevant facts, or the obligation needs a different theory view. This paper presents Schwarz, an agentic verification harness that makes SMT-backed proof failure local, checkable, and repairable. Schwarz turns failed verification into obligation-local repair tasks: program-point snapshots expose checked facts at a boundary, local lemmas let the agent propose missing proof steps, and theory-aware solver policies guide the agent toward solver-friendly formulations for numeric, quantified, memory, and floating-point obligations. We implement Schwarz for C and Rust/Verus and evaluate it on 1,475 tasks. On 475 benchmarks from recent agentic verification tools, Schwarz solves 95.2% of the tasks. On 1,000 tasks from the SV-COMP 2026 ReachSafety track, averaging 1,427 LOC, Schwarz solves 91.5% of the tasks, compared with 60.1% for CPAchecker. Ablations and comparison with a pure-agent baseline show that solver-aware repair is effective and scalable.

Problem

• Agentic Verification
• SMT Solver
• Verification Error
• Repairable Proof Failure
• Theory-aware Policies

Innovation

• Solver-Aware
• Agentic Verification
• Obligation-Local Repair
• Theory-Aware Policies