[2603.01801] What Papers Don't Tell You: Recovering Tacit Knowledge for Automated Paper Reproduction

Computer Science > Artificial Intelligence arXiv:2603.01801 (cs) [Submitted on 2 Mar 2026] 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 View a PDF of the paper titled What Papers Don't Tell You: Recovering Tacit Knowledge for Automated Paper Reproduction, by Lehui Li and 10 other authors View PDF 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 of...