Beyond Language Barriers: Multi-Agent Coordination for Multi-Language Code Generation

• URL: http://arxiv.org/abs/2509.19918v1
• Date: Wed, 24 Sep 2025 09:18:08 GMT
• Title: Beyond Language Barriers: Multi-Agent Coordination for Multi-Language Code Generation
• Authors: Micheline Bénédicte Moumoula, Serge Lionel Nikiema, Albérick Euraste Djire, Abdoul Kader Kabore, Jacques Klein, Tegawendé F. Bissyande,
• Abstract summary: XL-CoGen produces high-quality code across multiple programming languages.<n>It integrates intermediate representation, code generation, translation, and automated repair.
• Score: 8.896718697354187
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Producing high-quality code across multiple programming languages is increasingly important as today's software systems are built on heterogeneous stacks. Large language models (LLMs) have advanced the state of automated programming, yet their proficiency varies sharply between languages, especially those with limited training data such as Rust, Perl, OCaml, and Erlang. Many current solutions including language-specific fine-tuning, multi-agent orchestration, transfer learning, and intermediate-representation pipelines still approach each target language in isolation, missing opportunities to share knowledge or exploit recurring cross-language patterns. XL-CoGen tackles this challenge with a coordinated multi-agent architecture that integrates intermediate representation, code generation, translation, and automated repair. Its distinguishing feature is a data-driven mechanism for selecting bridging languages: empirically derived transfer matrices identify the best intermediate languages based on demonstrated translation success rather than raw generation accuracy. The system performs early output validation, iteratively corrects errors, and reuses intermediate artifacts as contextual scaffolds for subsequent translations. Extensive experiments show that XL-CoGen yields notable improvements with 13 percentage-point gains over the strongest fine-tuned baseline and as much as 30 percentage points over existing single-language multi-agent methods. Ablation studies further demonstrate that compatibility-guided bridging significantly outperforms LLM-based heuristics, confirming the value of cumulative cross-language knowledge transfer.

Related papers

• What Drives Cross-lingual Ranking? Retrieval Approaches with Multilingual Language Models [0.19116784879310025]
  Cross-lingual information retrieval is challenging due to disparities in resources, scripts, and weak cross-lingual semantic alignment in embedding models.<n>Existing pipelines often rely on translation and monolingual retrievals, which add computational overhead and noise, performance.<n>This work systematically evaluates four intervention types, namely document translation, multilingual dense retrieval with pretrained encoders, contrastive learning at word, phrase, and query-document levels, and cross-encoder re-ranking, across three benchmark datasets.
  arXiv  Detail & Related papers  (2025-11-24T17:17:40Z)
• Tracing Multilingual Representations in LLMs with Cross-Layer Transcoders [51.380449540006985]
  Large Language Models (LLMs) can process many languages, yet how they internally represent this diversity remains unclear.<n>Do they form shared multilingual representations with language-specific decoding, and if so, why does performance still favor the dominant training language?<n>We analyze their internal mechanisms using cross-layer transcoders (CLT) and attribution graphs.
  arXiv  Detail & Related papers  (2025-11-13T22:51:06Z)
• Language Drift in Multilingual Retrieval-Augmented Generation: Characterization and Decoding-Time Mitigation [11.110312833458421]
  We study output language drift in multilingual RAG across multiple datasets, languages, and LLM backbones.<n>Our experiments reveal that the drift results from decoder-level collapse, where dominant token distributions and high-frequency English patterns dominate the intended generation language.<n>We propose Soft Constrained Decoding (SCD), a lightweight, training-free decoding strategy that gently steers generation toward the target language.
  arXiv  Detail & Related papers  (2025-11-13T05:36:31Z)
• InterTrans: Leveraging Transitive Intermediate Translations to Enhance LLM-based Code Translation [9.655135415596414]
  Code translation aims to convert a program from one programming language (PL) to another. Recent studies show that even advanced techniques such as large language models (LLMs) still struggle with the task. We introduce InterTrans, an LLM-based automated code translation approach.
  arXiv  Detail & Related papers  (2024-11-01T22:31:32Z)
• Bridging the Language Gaps in Large Language Models with Inference-Time Cross-Lingual Intervention [71.12193680015622]
  Large Language Models (LLMs) have shown remarkable capabilities in natural language processing. LLMs exhibit significant performance gaps among different languages. We propose Inference-Time Cross-Lingual Intervention (INCLINE) to overcome these limitations without incurring significant costs.
  arXiv  Detail & Related papers  (2024-10-16T11:23:03Z)
• Multi-Programming Language Ensemble for Code Generation in Large Language Model [5.882816711878273]
  Large language models (LLMs) have significantly improved code generation, particularly in one-pass code generation. Most existing approaches focus solely on generating code in a single programming language, overlooking the potential of leveraging the multi-language capabilities of LLMs. We propose Multi-Programming Language Ensemble (MPLE), a novel ensemble-based method that utilizes code generation across multiple programming languages to enhance overall performance.
  arXiv  Detail & Related papers  (2024-09-06T08:31:18Z)
• Assessing Code Generation with Intermediate Languages [6.999311675957218]
  This study explores the utilization of intermediate languages, including various programming languages, natural language solutions, and pseudo-code. Our findings reveal that intermediate languages generally exhibit greater efficacy in larger models that have not yet achieved state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-07-07T15:35:41Z)
• IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators [49.903001442804594]
  This work investigates the prospect of leveraging compiler intermediate representations (IR) to improve the multilingual capabilities of Code-LMs. We first compile SLTrans, a parallel dataset consisting of nearly 4M self-contained source code files. Next, we carry out continued causal language modelling training on SLTrans, forcing the Code-LMs to learn the IR language. Our resulting models, dubbed IRCoder, display sizeable and consistent gains across a wide variety of code generation tasks and metrics.
  arXiv  Detail & Related papers  (2024-03-06T17:52:08Z)
• AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection [69.79627042058048]
  AdaCCD is a novel cross-lingual adaptation method that can detect cloned codes in a new language without annotations in that language. We evaluate the cross-lingual adaptation results of AdaCCD by constructing a multilingual code clone detection benchmark consisting of 5 programming languages.
  arXiv  Detail & Related papers  (2023-11-13T12:20:48Z)
• Cross-lingual Transfer in Programming Languages: An Extensive Empirical Study [5.350495525141013]
  Large language models (LLMs) have achieved state-of-the-art performance in various software engineering tasks.<n>critical languages, such as Rust and Swift, remain low-resource due to limited openly available code.<n>We develop a performance prediction model to guess the best source languages for a given target and task.
  arXiv  Detail & Related papers  (2023-10-25T19:04:33Z)
• T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification [50.675552118811]
  Cross-lingual text classification is typically built on large-scale, multilingual language models (LMs) pretrained on a variety of languages of interest. We propose revisiting the classic "translate-and-test" pipeline to neatly separate the translation and classification stages.
  arXiv  Detail & Related papers  (2023-06-08T07:33:22Z)
• On Cross-Lingual Retrieval with Multilingual Text Encoders [51.60862829942932]
  We study the suitability of state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks. We benchmark their performance in unsupervised ad-hoc sentence- and document-level CLIR experiments. We evaluate multilingual encoders fine-tuned in a supervised fashion (i.e., we learn to rank) on English relevance data in a series of zero-shot language and domain transfer CLIR experiments.
  arXiv  Detail & Related papers  (2021-12-21T08:10:27Z)
• FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding [85.29270319872597]
  We propose an enhanced fusion method that takes cross-lingual data as input for XLM finetuning. During inference, the model makes predictions based on the text input in the target language and its translation in the source language. To tackle this issue, we propose an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language.
  arXiv  Detail & Related papers  (2020-09-10T22:42:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.