KVerus: Scalable and Resilient Formal Verification Proof Generation for Rust Code
Yuwei Liu, Xinyi Wan, Yanhao Wang, Minghua Wang, Lin Huang, Tao Wei

TL;DR
KVerus is a retrieval-augmented system that enhances formal verification of Rust code by handling complex dependencies and evolving codebases, achieving higher verification success rates.
Contribution
It introduces a self-adaptive verification paradigm with a dynamic knowledge base, improving proof synthesis and repair in Rust verification compared to existing methods.
Findings
Verifies 80.2% of single-file benchmarks, outperforming AutoVerus (56.9%)
Achieves 51.0% success on cross-file repository benchmarks, versus 4.5% baseline
Produces proofs for 23 functions in Rust OS kernel's memory-management module
Abstract
Formal verification provides the highest assurance of software correctness and security, but its application to large-scale, evolving systems remains a major challenge. While large language models (LLMs) have shown promise in automating proof generation, they often fail in real-world settings due to their inability to handle complex cross-module dependencies or changes in the codebase or the verification toolchain. We identify the fundamental problem as the Semantic-Structural Gap: LLMs operate on semantic code patterns, whereas formal verification is governed by rigid structural dependencies, a disconnect that leads to brittle, unsustainable proofs. To bridge this gap, we propose a new paradigm of self-adaptive verification and present KVerus, a retrieval-augmented system for Verus-based Rust verification that can adapt to an evolving software environment. KVerus constructs a dynamic…
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