• Paper: Large Language Model-Powered Agent for C to Rust Code Translatio

    From John R Levine@21:1/5 to All on Fri May 23 13:12:38 2025
    Another paper claims their LLM with feedback works pretty well.

    Abstract

    The C programming language has been foundational in building system-level software. However, its manual memory management model frequently leads to memory safety issues. In response, a modern system programming language,
    Rust, has emerged as a memory-safe alternative. Moreover, automating the C-to-Rust translation empowered by the rapid advancements of the
    generative capabilities of LLMs is gaining growing interest for large
    volumes of legacy C code. Despite some success, existing LLM-based
    approaches have constrained the role of LLMs to static prompt-response
    behavior and have not explored their agentic problem-solving capability. Applying the LLM agentic capability for the C-to-Rust translation
    introduces distinct challenges, as this task differs from the traditional
    LLM agent applications, such as math or commonsense QA domains. First, the scarcity of parallel C-to-Rust datasets hinders the retrieval of suitable
    code translation exemplars for in-context learning. Second, unlike math or commonsense QA, the intermediate steps required for C-to-Rust are not well-defined. Third, it remains unclear how to organize and cascade these intermediate steps to construct a correct translation trajectory. To
    address these challenges in the C-to-Rust translation, we propose a novel intermediate step, the Virtual Fuzzing-based equivalence Test (VFT), and
    an agentic planning framework, the LLM-powered Agent for C-to-Rust code translation (LAC2R). The VFT guides LLMs to identify input arguments that induce divergent behaviors between an original C function and its Rust counterpart and to generate informative diagnoses to refine the unsafe
    Rust code. LAC2R uses the MCTS to systematically organize the LLM-induced intermediate steps for correct translation. We experimentally demonstrated
    that LAC2R effectively conducts C-to-Rust translation on large-scale, real-world benchmarks.

    https://arxiv.org/abs/2505.15858

    Regards,
    John Levine, johnl@taugh.com, Taughannock Networks, Trumansburg NY
    Please consider the environment before reading this e-mail. https://jl.ly

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