Adversarial Agent Collaboration for C to Rust Translation

• URL: http://arxiv.org/abs/2510.03879v1
• Date: Sat, 04 Oct 2025 17:08:36 GMT
• Title: Adversarial Agent Collaboration for C to Rust Translation
• Authors: Tianyu Li, Ruishi Li, Bo Wang, Brandon Paulsen, Umang Mathur, Prateek Saxena,
• Abstract summary: ACToR translates all of the 63 real-world command line utilities considered in our benchmarks.<n>It achieves over 90% test pass rate with zero human intervention.<n>It is the first such system that reliably translates C programs of this scale.
• Score: 13.65848262530917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Translating C to memory-safe languages, like Rust, prevents critical memory safety vulnerabilities that are prevalent in legacy C software. Existing approaches for C to safe Rust translation, including LLM-assisted ones, do not generalize on larger (> 500 LoC) C codebases because they depend on complex program analyses that frequently break. In this work, we present ACToR (Adversarial C To Rust translator), a simple LLM agent-based approach. Inspired by GANs, ACToR pits a generator agent against a discriminator agent, which collaborate to iteratively generate a Rust translation. On each iteration, the translator agent synthesizes and refines a Rust translation to pass an existing suite of tests, and then the discriminator agent finds new failing tests. We demonstrate that ACToR translates all of the 63 real-world command line utilities considered in our benchmarks, which have an average size of 485 lines of code, and it achieves over 90% test pass rate with zero human intervention. To our knowledge, it is the first such system that reliably translates C programs of this scale. Furthermore, ACToR improves translation correctness by up to 18.9% compared to baseline, non-adversarial approaches.

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