Automated Proof Generation for Rust Code via Self-Evolution

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...

arxiv.org

Can LLMs Enable Verification in Mainstream Programming?

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...

arxiv.org

Zac Hatfield-Dodds – Formal Verification is Overrated [Alignment Workshop]

YouTube video by FAR․AI

youtu.be

Proving the Coding Interview: A Benchmark for Formally Verified Code Generation

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 ...

arxiv.org

LLM-Assisted Static Analysis for Detecting Security Vulnerabilities

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...

arxiv.org

AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement

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 ...

arxiv.org

The VerifAI Workshop

VerifAI: AI Verification in the Wild @ ICLR 2025

verifai-workshop.github.io

Laurel: Generating Dafny Assertions Using Large Language Models

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...

arxiv.org

Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs

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...

arxiv.org

Prevent factual errors from LLM hallucinations with mathematically sound Automated Reasoning checks (preview) | Amazon Web Services

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.

aws.amazon.com

From Naptime to Big Sleep: Using Large Language Models To Catch Vulnerabilities In Real-World Code

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

googleprojectzero.blogspot.com

Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming

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...

arxiv.org

Grammar-Aligned Decoding

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...

arxiv.org

Evaluating frontier AI R&D capabilities of language model agents against human experts

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...

metr.org

Arithmetic Without Algorithms: Language Models Solve Math With a Bag of Heuristics

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...

arxiv.org