@inproceedings{hu-etal-2026-reproagent,
title = "{R}epro{A}gent: Contract-Guided Paper-to-Code Reproduction",
author = "Hu, Xue and
Zewei, Pan and
Wang, Zhongyuan and
Liu, Zhou and
Su, Zeli and
Zhang, Wentao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.175/",
pages = "3681--3702",
ISBN = "979-8-89176-462-0",
abstract = "Paper-to-code reproduction asks scientific AI agents to turn research papers into executable repositories that preserve the paper{'}s method, protocol and artifacts. This is difficult because the specification is split: explicit paper content such as algorithms, metrics and artifacts is often lost across long agent trajectories, while implicit details such as framework defaults and conventions inherited from related work are absent from the paper. We introduce ReproAgent a four-stage Prepare{--}Plan{--}Generate{--}Repair pipeline built around a persistent implementation contract with two channels: an implementation-requirement channel that turns paper snippets into code obligations, and a reference-evidence channel that retrieves content and structure evidence from related repositories. Both are bound to work packages, projected into file-level contracts, and consumed across generation and repair. On PaperBench Code-Dev, ReproAgent reaches the highest mean score among same-backbone scaffolds under both Claude-Sonnet-4.5 and Gemini-3-Flash. End-to-end channel ablations and per-paper cases support the contribution of both channels.Code and experimental artifacts are publicly available at https://anonymous.4open.science/r/ReproAgent-E760."
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<abstract>Paper-to-code reproduction asks scientific AI agents to turn research papers into executable repositories that preserve the paper’s method, protocol and artifacts. This is difficult because the specification is split: explicit paper content such as algorithms, metrics and artifacts is often lost across long agent trajectories, while implicit details such as framework defaults and conventions inherited from related work are absent from the paper. We introduce ReproAgent a four-stage Prepare–Plan–Generate–Repair pipeline built around a persistent implementation contract with two channels: an implementation-requirement channel that turns paper snippets into code obligations, and a reference-evidence channel that retrieves content and structure evidence from related repositories. Both are bound to work packages, projected into file-level contracts, and consumed across generation and repair. On PaperBench Code-Dev, ReproAgent reaches the highest mean score among same-backbone scaffolds under both Claude-Sonnet-4.5 and Gemini-3-Flash. End-to-end channel ablations and per-paper cases support the contribution of both channels.Code and experimental artifacts are publicly available at https://anonymous.4open.science/r/ReproAgent-E760.</abstract>
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%0 Conference Proceedings
%T ReproAgent: Contract-Guided Paper-to-Code Reproduction
%A Hu, Xue
%A Zewei, Pan
%A Wang, Zhongyuan
%A Liu, Zhou
%A Su, Zeli
%A Zhang, Wentao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F hu-etal-2026-reproagent
%X Paper-to-code reproduction asks scientific AI agents to turn research papers into executable repositories that preserve the paper’s method, protocol and artifacts. This is difficult because the specification is split: explicit paper content such as algorithms, metrics and artifacts is often lost across long agent trajectories, while implicit details such as framework defaults and conventions inherited from related work are absent from the paper. We introduce ReproAgent a four-stage Prepare–Plan–Generate–Repair pipeline built around a persistent implementation contract with two channels: an implementation-requirement channel that turns paper snippets into code obligations, and a reference-evidence channel that retrieves content and structure evidence from related repositories. Both are bound to work packages, projected into file-level contracts, and consumed across generation and repair. On PaperBench Code-Dev, ReproAgent reaches the highest mean score among same-backbone scaffolds under both Claude-Sonnet-4.5 and Gemini-3-Flash. End-to-end channel ablations and per-paper cases support the contribution of both channels.Code and experimental artifacts are publicly available at https://anonymous.4open.science/r/ReproAgent-E760.
%U https://aclanthology.org/2026.findings-emnlp.175/
%P 3681-3702
Markdown (Informal)
[ReproAgent: Contract-Guided Paper-to-Code Reproduction](https://aclanthology.org/2026.findings-emnlp.175/) (Hu et al., Findings 2026)
ACL
- Xue Hu, Pan Zewei, Zhongyuan Wang, Zhou Liu, Zeli Su, and Wentao Zhang. 2026. ReproAgent: Contract-Guided Paper-to-Code Reproduction. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 3681–3702, Budapest, Hungary. Association for Computational Linguistics.