# BabelCoder: Agentic Code Translation with Specification Alignment

arXiv: https://arxiv.org/abs/2512.06902 · HTML: https://mlsanity.com/paper/2512.06902/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 28 of its 51 references that are in the map.
- First posted on arXiv 7 Dec 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 28 of 28)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges](https://mlsanity.com/paper/2401.07339/) (2024)
- [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020)
- [Self-collaboration Code Generation via ChatGPT](https://mlsanity.com/paper/2304.07590/) (2023)
- [CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks](https://mlsanity.com/paper/2105.12655/) (2021)
- [Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimization](https://mlsanity.com/paper/2404.02183/) (2024)
- [Leveraging Automated Unit Tests for Unsupervised Code Translation](https://mlsanity.com/paper/2110.06773/) (2021)
- [AVATAR: A Parallel Corpus for Java-Python Program Translation](https://mlsanity.com/paper/2108.11590/) (2021)
- [Exploring and Unleashing the Power of Large Language Models in Automated Code Translation](https://mlsanity.com/paper/2404.14646/) (2024)
- [Code Translation with Compiler Representations](https://mlsanity.com/paper/2207.03578/) (2022)
- [Teaching Code LLMs to Use Autocompletion Tools in Repository-Level Code Generation](https://mlsanity.com/paper/2401.06391/) (2024)
- [AlphaTrans: A Neuro-Symbolic Compositional Approach for Repository-Level Code Translation and Validation](https://mlsanity.com/paper/2410.24117/) (2024)
- [Bias Unveiled: Investigating Social Bias in LLM-Generated Code](https://mlsanity.com/paper/2411.10351/) (2024)
- [Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation](https://mlsanity.com/paper/2407.19619/) (2024)
- [InterTrans: Leveraging Transitive Intermediate Translations to Enhance LLM-based Code Translation](https://mlsanity.com/paper/2411.01063/) (2024)
- [Ownership guided C to Rust translation](https://mlsanity.com/paper/2303.10515/) (2023)
- [ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation](https://mlsanity.com/paper/2501.18460/) (2025)
- [MANTRA: Enhancing Automated Method-Level Refactoring with Contextual RAG and Multi-Agent LLM Collaboration](https://mlsanity.com/paper/2503.14340/) (2025)
- [An Exploratory Study on Fine-Tuning Large Language Models for Secure Code Generation](https://mlsanity.com/paper/2408.09078/) (2024)
- [Syntax and Domain Aware Model for Unsupervised Program Translation](https://mlsanity.com/paper/2302.03908/) (2023)
- [Assessing and Improving Syntactic Adversarial Robustness of Pre-trained Models for Code Translation](https://mlsanity.com/paper/2310.18587/) (2023)
- [A Multi-Language Perspective on the Robustness of LLM Code Generation](https://mlsanity.com/paper/2504.19108/) (2025)
- [Specification-Driven Code Translation Powered by Large Language Models: How Far Are We?](https://mlsanity.com/paper/2412.04590/) (2024)
- [RobuNFR: Evaluating the Robustness of Large Language Models on Non-Functional Requirements Aware Code Generation](https://mlsanity.com/paper/2503.22851/) (2025)

## Built on by (most cited first, 1 of 1)

- [Social Bias in LLM-Generated Code: Benchmark and Mitigation](https://mlsanity.com/paper/2605.00382/) (2026)

## Abstract (arXiv)

As software systems evolve, developers increasingly work across multiple programming languages and often face the need to migrate code from one language to another. While automatic code translation offers a promising solution, it has long remained a challenging task. Recent advancements in Large Language Models (LLMs) have shown potential for this task, yet existing approaches remain limited in accuracy and fail to effectively leverage contextual and structural cues within the code. Prior work has explored translation and repair mechanisms, but lacks a structured, agentic framework where multiple specialized agents collaboratively improve translation quality. In this work, we introduce BabelCoder, an agentic framework that performs code translation by decomposing the task into specialized agents for translation, testing, and refinement, each responsible for a specific aspect such as generating code, validating correctness, or repairing errors. We evaluate BabelCoder on four benchmark datasets and compare it against four state-of-the-art baselines. BabelCoder outperforms existing methods by 0.5%-13.5% in 94% of cases, achieving an average accuracy of 94.16%.