@inproceedings{chen-etal-2026-paircoder,
title = "{P}air{C}oder: Pair Programming-Inspired Two-Agent Collaboration for Code Generation",
author = "Chen, Junhao and
Li, Xiang and
Xu, Yibin and
Cui, Yuehan and
Weng, Fangsheng and
Zhao, Hao and
Ma, Fei and
Tian, Qi",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.149/",
doi = "10.18653/v1/2026.findings-acl.149",
pages = "3043--3058",
ISBN = "979-8-89176-395-1",
abstract = "Large Language Models (LLMs) achieve strong results on code generation, but single model inference remains brittle on tasks that require iterative refinement. Existing multi agent frameworks improve reliability, yet they often incur substantial token and latency overhead. We introduce PairCoder, a framework that brings pair programming to autonomous LLM collaboration. PairCoder assigns one model to code generation and the other to review, and switches roles when repeated errors suggest that the current interaction has stalled. Across 13 LLMs on HumanEval, PairCoder consistently improves over single model inference. On eight representative backbones, it reaches 91.0{\%} pass@1 and improves over single model inference by up to 20.3{\%} while reducing token usage by 40{\%} to 70{\%} relative to multi agent baselines. Many heterogeneous pairings also outperform both constituent models, suggesting that the framework generalizes across model families. These results position PairCoder as an effective and deployment conscious alternative to heavier multi agent systems.Code is available at \url{https://github.com/yisuanwang/PairCoder}"
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<abstract>Large Language Models (LLMs) achieve strong results on code generation, but single model inference remains brittle on tasks that require iterative refinement. Existing multi agent frameworks improve reliability, yet they often incur substantial token and latency overhead. We introduce PairCoder, a framework that brings pair programming to autonomous LLM collaboration. PairCoder assigns one model to code generation and the other to review, and switches roles when repeated errors suggest that the current interaction has stalled. Across 13 LLMs on HumanEval, PairCoder consistently improves over single model inference. On eight representative backbones, it reaches 91.0% pass@1 and improves over single model inference by up to 20.3% while reducing token usage by 40% to 70% relative to multi agent baselines. Many heterogeneous pairings also outperform both constituent models, suggesting that the framework generalizes across model families. These results position PairCoder as an effective and deployment conscious alternative to heavier multi agent systems.Code is available at https://github.com/yisuanwang/PairCoder</abstract>
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%0 Conference Proceedings
%T PairCoder: Pair Programming-Inspired Two-Agent Collaboration for Code Generation
%A Chen, Junhao
%A Li, Xiang
%A Xu, Yibin
%A Cui, Yuehan
%A Weng, Fangsheng
%A Zhao, Hao
%A Ma, Fei
%A Tian, Qi
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F chen-etal-2026-paircoder
%X Large Language Models (LLMs) achieve strong results on code generation, but single model inference remains brittle on tasks that require iterative refinement. Existing multi agent frameworks improve reliability, yet they often incur substantial token and latency overhead. We introduce PairCoder, a framework that brings pair programming to autonomous LLM collaboration. PairCoder assigns one model to code generation and the other to review, and switches roles when repeated errors suggest that the current interaction has stalled. Across 13 LLMs on HumanEval, PairCoder consistently improves over single model inference. On eight representative backbones, it reaches 91.0% pass@1 and improves over single model inference by up to 20.3% while reducing token usage by 40% to 70% relative to multi agent baselines. Many heterogeneous pairings also outperform both constituent models, suggesting that the framework generalizes across model families. These results position PairCoder as an effective and deployment conscious alternative to heavier multi agent systems.Code is available at https://github.com/yisuanwang/PairCoder
%R 10.18653/v1/2026.findings-acl.149
%U https://aclanthology.org/2026.findings-acl.149/
%U https://doi.org/10.18653/v1/2026.findings-acl.149
%P 3043-3058
Markdown (Informal)
[PairCoder: Pair Programming-Inspired Two-Agent Collaboration for Code Generation](https://aclanthology.org/2026.findings-acl.149/) (Chen et al., Findings 2026)
ACL
- Junhao Chen, Xiang Li, Yibin Xu, Yuehan Cui, Fangsheng Weng, Hao Zhao, Fei Ma, and Qi Tian. 2026. PairCoder: Pair Programming-Inspired Two-Agent Collaboration for Code Generation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 3043–3058, San Diego, California, United States. Association for Computational Linguistics.