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arXiv · 2603.01801

What Papers Don't Tell You: Recovering Tacit Knowledge for Automated Paper Reproduction

Lehui Li·Ruining Wang·Haochen Song·Yaoxin Mao·Tong Zhang·Yuyao Wang·Jiayi Fan·Yitong Zhang·Jieping Ye·Chengqi Zhang·Yongshun Gong
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Abstract

Automated paper reproduction -- generating executable code from academic papers -- is bottlenecked not by information retrieval but by the tacit knowledge that papers inevitably leave implicit. We formalize this challenge as the progressive recovery of three types of tacit knowledge -- relational, somatic, and collective -- and propose \method, a graph-based agent framework with a dedicated mechanism for each: node-level relation-aware aggregation recovers relational knowledge by analyzing implementation-unit-level reuse and adaptation relationships between the target paper and its citation neighbors; execution-feedback refinement recovers somatic knowledge through iterative debugging driven by runtime signals; and graph-level knowledge induction distills collective knowledge from clusters of papers sharing similar implementations. On an extended ReproduceBench spanning 3 domains, 10 tasks, and 40 recent papers, \method{} achieves an average performance gap of 10.04\% against official implementations, improving over the strongest baseline by 24.68\%. The code will be publicly released upon acceptance; the repository link will be provided in the final version.

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Lehui Li, Ruining Wang, Haochen Song, Yaoxin Mao, Tong Zhang, Yuyao Wang, Jiayi Fan, Yitong Zhang, Jieping Ye, Chengqi Zhang, Yongshun Gong. 2026-03-02. What Papers Don't Tell You: Recovering Tacit Knowledge for Automated Paper Reproduction. https://arxiv.org/abs/2603.01801

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