Harnessing Code Agents for Automatic Software Verification
Formal verification offers the strongest guarantee of software correctness, but it does not scale: the proofs demanded by interactive theorem provers such as Coq require enormous expert effort. Large language models (LLMs) promise to generate these proofs automatically, yet existing approaches wire a fixed, human-designed proof strategy into the system and constrain the model to follow it (retrieving premises and predicting tactics one step at a time, or splitting goals by divide-and-conquer), and still prove only a fraction of their target theorems. We show that imposing such a strategy is
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 54%kyegomez/Lets-Verify-Step-by-Step →
- PossiblePossibly related (embedding) · 52%agent-sh/agnix →
- PossiblePossibly related (embedding) · 51%sileod/reasoning-core →
- PossiblePossibly related (embedding) · 51%agent-tools →
- PossiblePossibly related (embedding) · 51%BoundaryML/baml →
- LinkedLinked via arxiv author · 85%Shuangxiang Kan →
“Harnessing Code Agents for Automatic Software Verification”
- LinkedLinked via arxiv author · 85%Shuanglong Kan →
“Harnessing Code Agents for Automatic Software Verification”
- LinkedLinked via arxiv author · 85%Sebastian Ertel →
“Harnessing Code Agents for Automatic Software Verification”