一句话：提出多智能体工作流 ReCodeAgent，实现全自动、语言无关的大规模仓库级代码翻译与验证。
现有仓库级代码翻译与验证技术多局限于单一源-目标语言对，适配新语言对需要大量工程投入。本文提出 ReCodeAgent，一种自主多智能体工作流：用户仅需提供源语言项目并指定目标语言，系统即自动翻译并验证整个仓库，且能自主调用各语言特有的分析工具，从而实现语言无关性。作者在 118 个真实项目（6 种语言、4 个语言对，平均每个项目 1,975 行代码、43 个翻译单元）上与四种神经符号及智能体基线比较，ReCodeAgent 的翻译正确性持续领先，地面真值测试通过率提升 60.8%，平均成本 15.3 美元；轨迹分析表明其流程高效，而单智能体架构下通过率平均下降 40.4%、轨迹变长 28%。
Most repository-level code translation and validation techniques have been evaluated on a single source-target programming language (PL) pair, owing to the complex engineering effort required to adapt new PL pairs. Programming agents can enable PL-agnosticism in repository-level code translation and validation: they can synthesize code across many PLs and autonomously use existing tools specific to each PL’s analysis. However, state-of-the-art has yet to offer a fully autonomous agentic approach for repository-level code translation and validation of large-scale programs. This paper proposes ReCodeAgent, an autonomous multi-agent approach for language-agnostic repository-level code translation and validation. Users only need to provide the project in the source PL and specify the target PL for ReCodeAgent to automatically translate and validate the entire repository. ReCodeAgent is the first technique to achieve high translation success rates across many PLs. We compare the effectiveness of ReCodeAgent with four alternative neuro-symbolic and agentic approaches to translate 118 real-world projects, with 1,975 LoC and 43 translation units for each project, on average. The projects cover 6 PLs and 4 PL pairs. Our results demonstrate that ReCodeAgent consistently outperforms prior techniques on translation correctness, improving test pass rate by 60.8% on ground-truth tests, with an average cost of $15.3. We also perform process-centric analysis of ReCodeAgent trajectories to confirm its procedural efficiency. Finally, we investigate how the design choices (a multi-agent vs. single-agent architecture) influence ReCodeAgent performance: on average, the test pass rate drops by 40.4%, and trajectories become 28% longer and persistently inefficient.
@inproceedings{abs-2604-07341,
title = {ReCodeAgent: A Multi-agent Workflow for Language-Agnostic Translation and Validation of Large-Scale Repositories},
author = {Ali Reza Ibrahimzada and Brandon Paulsen and Daniel Kroening and Reyhaneh Jabbarvand},
booktitle = {ASE},
year = {2026},
doi = {10.1145/3832783.3837486}
}