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

• URL: http://arxiv.org/abs/2603.01801v1
• Date: Mon, 02 Mar 2026 12:33:31 GMT
• Title: What Papers Don't Tell You: Recovering Tacit Knowledge for Automated Paper Reproduction
• Authors: Lehui Li, Ruining Wang, Haochen Song, Yaoxin Mao, Tong Zhang, Yuyao Wang, Jiayi Fan, Yitong Zhang, Jieping Ye, Chengqi Zhang, Yongshun Gong,
• Abstract summary: method is a graph-based agent framework for generating executable code from academic papers.<n>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.
• Score: 57.86097956633207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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