Ensuring correctness is crucial for code generation. Formal verification offers a definitive assurance of correctness, but demands substantial human effort in proof construction and hence raises a pre...

Although formal methods are capable of producing reliable software, they have seen minimal adoption in everyday programming. Automatic code generation using large language models is becoming increasin...

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We introduce the Formally Verified Automated Programming Progress Standards, or FVAPPS, a benchmark of 4715 samples for writing programs and proving their correctness, the largest formal verification ...

Software is prone to security vulnerabilities. Program analysis tools to detect them have limited effectiveness in practice due to their reliance on human labeled specifications. Large language models...

Automated code generation with large language models has gained significant traction, but there remains no guarantee on the correctness of generated code. We aim to use formal verification to provide ...

VerifAI: AI Verification in the Wild @ ICLR 2025

Dafny is a popular verification language, which automates proofs by outsourcing them to an SMT solver. This automation is not perfect, however, and the solver often requires guidance in the form of he...

The formalization of existing mathematical proofs is a notoriously difficult process. Despite decades of research on automation and proof assistants, writing formal proofs remains arduous and only acc...

Enhance conversational AI accuracy with Automated Reasoning checks - first and only gen AI safeguard that helps reduce hallucinations by encoding domain rules into verifiable policies.

Posted by the Big Sleep team Introduction In our previous post, Project Naptime: Evaluating Offensive Security Capabilities of Large L...

Proof-oriented programs mix computational content with proofs of program correctness. However, the human effort involved in programming and proving is still substantial, despite the use of Satisfiabil...

Large Language Models (LLMs) struggle with reliably generating highly structured outputs, such as program code, mathematical formulas, or well-formed markup. Constrained decoding approaches mitigate t...

We’re releasing RE-Bench, a new benchmark for measuring the performance of humans and frontier model agents on ML research engineering tasks. We also share data from 71 human expert attempts and resul...

Do large language models (LLMs) solve reasoning tasks by learning robust generalizable algorithms, or do they memorize training data? To investigate this question, we use arithmetic reasoning as a rep...