Harnessing Code Agents for Automatic Software Verification

📰 ArXiv cs.AI

Learn how code agents can automate software verification using large language models, reducing the need for manual proof efforts

advanced Published 8 Jul 2026

Action Steps

Implement a code agent framework using a large language model to generate proofs automatically
Train the model on a dataset of existing proofs to improve its accuracy
Integrate the code agent with an interactive theorem prover like Coq to leverage its capabilities
Test the code agent on a set of benchmark programs to evaluate its performance
Refine the model and its training data to improve its ability to generate correct proofs

Who Needs to Know This

Software engineers and researchers can benefit from this approach to improve the efficiency and accuracy of software verification, while reducing the workload of expert verifiers

Key Insight

💡 Code agents can leverage large language models to generate proofs automatically, reducing the need for manual effort and improving the efficiency of software verification

Key Takeaways

Learn how code agents can automate software verification using large language models, reducing the need for manual proof efforts

Full Article

Title: Harnessing Code Agents for Automatic Software Verification

Abstract:
arXiv:2607.06341v1 Announce Type: cross Abstract: 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

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