10 citations · 11 across the 6 of their papers we have counts for
6 papers
ExVerus: Verus Proof Repair via Counterexample Reasoning
Jun Yang, Yuechun Sun, Yi Wu +5
Large Language Models (LLMs) have shown promising results in automating formal verification. However, existing approaches treat proof generation as a static, end-to-end prediction…
Reducing the Costs of Proof Synthesis on Rust Systems by Scaling Up a Seed Training Set
Nongyu Di, Tianyu Chen, Shan Lu +6
Large Language Models (LLMs) are widely used for code generation. However, the correctness of code generated by LLMs remains a concern. A potential remedy to this concern is to hav…
VeruSAGE: A Study of Agent-Based Verification for Rust Systems
Chenyuan Yang, Natalie Neamtu, Chris Hawblitzel +2
Large language models (LLMs) have shown impressive capability to understand and develop code. However, their capability to rigorously reason about and prove code correctness remain…
What's in a Proof? Analyzing Expert Proof-Writing Processes in F* and Verus
Rijul Jain, Shraddha Barke, Gabriel Ebner +3
Proof-oriented programming languages (POPLs) empower developers to write code alongside formal correctness proofs, providing formal guarantees that the code adheres to specified re…
Automated Proof Generation for Rust Code via Self-Evolution
Tianyu Chen, Shuai Lu, Shan Lu +11
Ensuring correctness is crucial for code generation. Formal verification offers a definitive assurance of correctness, but demands substantial human effort in proof construction an…
AutoVerus: Automated Proof Generation for Rust Code
Chenyuan Yang, Xuheng Li, Md Rakib Hossain Misu +10
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 generatio…