@inproceedings{zhao-etal-2026-autoreproduce,
title = "{A}uto{R}eproduce: Automatic {AI} Experiment Reproduction with Paper Lineage",
author = "Zhao, Xuanle and
Sang, Zilin and
Li, Yuxuan and
Shi, Qi and
Zhao, Weilun and
Wang, Shuo and
Zhang, Duzhen and
Han, Xu and
Liu, Zhiyuan and
Sun, Maosong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1001/",
pages = "21920--21942",
ISBN = "979-8-89176-390-6",
abstract = "Efficient reproduction of research papers is pivotal to accelerating scientific progress. However, the increasing complexity of proposed methods often renders reproduction a labor-intensive endeavor, necessitating profound domain expertise.To address this, we introduce the paper lineage, which systematically mines implicit knowledge from the cited literature. This algorithm serves as the backbone of our proposed , a multi-agent framework designed to autonomously reproduce experimental code in a complete, end-to-end manner. To ensure code executability, incorporates a sampling-based unit testing strategy for rapid validation. To assess reproduction capabilities, we introduce , a benchmark featuring verified implementations, alongside comprehensive metrics for evaluating both reproduction and execution fidelity. Extensive evaluations on PaperBench and demonstrate that consistently surpasses existing baselines across all metrics. Notably, it yields substantial improvements in reproduction fidelity and final execution performance. The code is available at \url{https://github.com/AI9Stars/AutoReproduce}."
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<abstract>Efficient reproduction of research papers is pivotal to accelerating scientific progress. However, the increasing complexity of proposed methods often renders reproduction a labor-intensive endeavor, necessitating profound domain expertise.To address this, we introduce the paper lineage, which systematically mines implicit knowledge from the cited literature. This algorithm serves as the backbone of our proposed , a multi-agent framework designed to autonomously reproduce experimental code in a complete, end-to-end manner. To ensure code executability, incorporates a sampling-based unit testing strategy for rapid validation. To assess reproduction capabilities, we introduce , a benchmark featuring verified implementations, alongside comprehensive metrics for evaluating both reproduction and execution fidelity. Extensive evaluations on PaperBench and demonstrate that consistently surpasses existing baselines across all metrics. Notably, it yields substantial improvements in reproduction fidelity and final execution performance. The code is available at https://github.com/AI9Stars/AutoReproduce.</abstract>
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%0 Conference Proceedings
%T AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage
%A Zhao, Xuanle
%A Sang, Zilin
%A Li, Yuxuan
%A Shi, Qi
%A Zhao, Weilun
%A Wang, Shuo
%A Zhang, Duzhen
%A Han, Xu
%A Liu, Zhiyuan
%A Sun, Maosong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhao-etal-2026-autoreproduce
%X Efficient reproduction of research papers is pivotal to accelerating scientific progress. However, the increasing complexity of proposed methods often renders reproduction a labor-intensive endeavor, necessitating profound domain expertise.To address this, we introduce the paper lineage, which systematically mines implicit knowledge from the cited literature. This algorithm serves as the backbone of our proposed , a multi-agent framework designed to autonomously reproduce experimental code in a complete, end-to-end manner. To ensure code executability, incorporates a sampling-based unit testing strategy for rapid validation. To assess reproduction capabilities, we introduce , a benchmark featuring verified implementations, alongside comprehensive metrics for evaluating both reproduction and execution fidelity. Extensive evaluations on PaperBench and demonstrate that consistently surpasses existing baselines across all metrics. Notably, it yields substantial improvements in reproduction fidelity and final execution performance. The code is available at https://github.com/AI9Stars/AutoReproduce.
%U https://aclanthology.org/2026.acl-long.1001/
%P 21920-21942
Markdown (Informal)
[AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage](https://aclanthology.org/2026.acl-long.1001/) (Zhao et al., ACL 2026)
ACL
- Xuanle Zhao, Zilin Sang, Yuxuan Li, Qi Shi, Weilun Zhao, Shuo Wang, Duzhen Zhang, Xu Han, Zhiyuan Liu, and Maosong Sun. 2026. AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 21920–21942, San Diego, California, United States. Association for Computational Linguistics.