# AutoVerus: Automated Proof Generation for Rust Code

arXiv: https://arxiv.org/abs/2409.13082 · HTML: https://mlsanity.com/paper/2409.13082/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 56 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Builds on (most cited first, 24 of 24)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [LeanDojo: Theorem Proving with Retrieval-Augmented Language Models](https://mlsanity.com/paper/2306.15626/) (2023)
- [Generative Language Modeling for Automated Theorem Proving](https://mlsanity.com/paper/2009.03393/) (2020)
- [Baldur: Whole-Proof Generation and Repair with Large Language Models](https://mlsanity.com/paper/2303.04910/) (2023)
- [Learning to Prove Theorems via Interacting with Proof Assistants](https://mlsanity.com/paper/1905.09381/) (2019)
- [LEGO-Prover: Neural Theorem Proving with Growing Libraries](https://mlsanity.com/paper/2310.00656/) (2023)
- [Large Language Models are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models](https://mlsanity.com/paper/2212.14834/) (2022)
- [Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers](https://mlsanity.com/paper/2205.10893/) (2022)
- [DebugBench: Evaluating Debugging Capability of Large Language Models](https://mlsanity.com/paper/2401.04621/) (2024)
- [Clover: Closed-Loop Verifiable Code Generation](https://mlsanity.com/paper/2310.17807/) (2023)
- [Towards AI-Assisted Synthesis of Verified Dafny Methods](https://mlsanity.com/paper/2402.00247/) (2024)
- [A Survey on Deep Learning for Theorem Proving](https://mlsanity.com/paper/2404.09939/) (2024)
- [DafnyBench: A Benchmark for Formal Software Verification](https://mlsanity.com/paper/2406.08467/) (2024)
- [Verus: Verifying Rust Programs using Linear Ghost Types (extended version)](https://mlsanity.com/paper/2303.05491/) (2023)
- [Lemur: Integrating Large Language Models in Automated Program Verification](https://mlsanity.com/paper/2310.04870/) (2023)
- [Generating Correctness Proofs with Neural Networks](https://mlsanity.com/paper/1907.07794/) (2019)
- [WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models](https://mlsanity.com/paper/2310.15991/) (2023)
- [Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming](https://mlsanity.com/paper/2405.01787/) (2024)
- [KernelGPT: Enhanced Kernel Fuzzing via Large Language Models](https://mlsanity.com/paper/2401.00563/) (2023)
- [Aeneas: Rust Verification by Functional Translation](https://mlsanity.com/paper/2206.07185/) (2022)
- [3DGen: AI-Assisted Generation of Provably Correct Binary Format Parsers](https://mlsanity.com/paper/2404.10362/) (2024)
- [Diffy: Inductive Reasoning of Array Programs using Difference Invariants](https://mlsanity.com/paper/2105.14748/) (2021)

## Built on by (most cited first, 30 of 49)

- [Formal Mathematical Reasoning: A New Frontier in AI](https://mlsanity.com/paper/2412.16075/) (2024)
- [AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement](https://mlsanity.com/paper/2412.06176/) (2024)
- [VERINA: Benchmarking Verifiable Code Generation](https://mlsanity.com/paper/2505.23135/) (2025)
- [Automated Proof Generation for Rust Code via Self-Evolution](https://mlsanity.com/paper/2410.15756/) (2024)
- [KNighter: Transforming Static Analysis with LLM-Synthesized Checkers](https://mlsanity.com/paper/2503.09002/) (2025)
- [A benchmark for vericoding: formally verified program synthesis](https://mlsanity.com/paper/2509.22908/) (2025)
- [Proving the Coding Interview: A Benchmark for Formally Verified Code Generation](https://mlsanity.com/paper/2502.05714/) (2025)
- [Propose, Solve, Verify: Self-Play Through Formal Verification](https://mlsanity.com/paper/2512.18160/) (2025)
- [VeruSAGE: A Study of Agent-Based Verification for Rust Systems](https://mlsanity.com/paper/2512.18436/) (2025)
- [Locus: Agentic Predicate Synthesis for Directed Fuzzing](https://mlsanity.com/paper/2508.21302/) (2025)
- [VeriStruct: AI-assisted Automated Verification of Data-Structure Modules in Verus](https://mlsanity.com/paper/2510.25015/) (2025)
- [ProofWright: Towards Agentic Formal Verification of CUDA](https://mlsanity.com/paper/2511.12294/) (2025)
- [TraceCoder: A Trace-Driven Multi-Agent Framework for Automated Debugging of LLM-Generated Code](https://mlsanity.com/paper/2602.06875/) (2026)
- [FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style Reasoning](https://mlsanity.com/paper/2604.11556/) (2026)
- [RAG-Verus: Repository-Level Program Verification with LLMs using Retrieval Augmented Generation](https://mlsanity.com/paper/2502.05344/) (2025)
- [Towards Repository-Level Program Verification with Large Language Models](https://mlsanity.com/paper/2509.25197/) (2025)
- [Neural Theorem Proving for Verification Conditions: A Real-World Benchmark](https://mlsanity.com/paper/2601.18944/) (2026)
- [Inductive Deductive Synthesis: Enabling AI to Generate Formally Verified Systems](https://mlsanity.com/paper/2605.23109/) (2026)
- [Agentic Specification Generator for Move Programs](https://mlsanity.com/paper/2509.24515/) (2025)
- [Designing LLM-based Multi-Agent Systems for Software Engineering Tasks: Quality Attributes, Design Patterns and Rationale](https://mlsanity.com/paper/2511.08475/) (2025)
- [VeriContest: A Competitive-Programming Benchmark for Verifiable Code Generation](https://mlsanity.com/paper/2605.08553/) (2026)
- [SymGPT: Auditing Smart Contracts via Combining Symbolic Execution with Large Language Models](https://mlsanity.com/paper/2502.07644/) (2025)
- [SysMoBench: Evaluating AI on Formally Modeling Complex Real-World Systems](https://mlsanity.com/paper/2509.23130/) (2025)
- [MINIF2F-DAFNY: LLM-Guided Mathematical Theorem Proving via Auto-Active Verification](https://mlsanity.com/paper/2512.10187/) (2025)
- [Doc2Spec: Synthesizing Formal Programming Specifications from Natural Language via Grammar Induction](https://mlsanity.com/paper/2602.04892/) (2026)
- [Spec-Harness: Assessing and Guiding LLM-Synthesized Formal Specifications Beyond Verifiability](https://mlsanity.com/paper/2604.00280/) (2026)
- [Certified Program Synthesis with a Multi-Modal Verifier](https://mlsanity.com/paper/2604.16584/) (2026)
- [P3: Joint Program-and-Proof Planning for Verified Code Generation](https://mlsanity.com/paper/2608.09277/) (2026)
- [Vero: Can AI Agents Build Formally Verified Software Repositories?](https://mlsanity.com/paper/2608.13522/) (2026)
- [Can LLM Aid in Solving Constraints with Inductive Definitions?](https://mlsanity.com/paper/2603.03668/) (2026)

## Abstract (arXiv)

Generative AI has shown its values for many software engineering tasks. Still in its infancy, large language model (LLM)-based proof generation lags behind LLM-based code generation. In this paper, we present AutoVerus. AutoVerus uses LLMs to automatically generate correctness proof for Rust code. AutoVerus is designed to match the unique features of Verus, a verification tool that can prove the correctness of Rust code using proofs and specifications also written in Rust. AutoVerus consists of a network of LLM agents that are crafted and orchestrated to mimic human experts' three phases of proof construction: preliminary proof generation, proof refinement guided by generic tips, and proof debugging guided by verification errors. To thoroughly evaluate AutoVerus and help foster future research in this direction, we have built a benchmark suite of 150 non-trivial proof tasks, based on existing code-generation benchmarks and verification benchmarks. Our evaluation shows that AutoVerus can automatically generate correct proof for more than 90% of them, with more than half of them tackled in less than 30 seconds or 3 LLM calls.