PaperCompiler is a framework that translates research papers into repository-level code by compiling paper-grounded evidence into explicit implementation specifications. It preserves source provenance, distinguishes between paper-supported, inferred, externally delegated, and unresolved information, and encodes non-degradation requirements, ownership, cross-file dependencies, and file-level constraints. The approach outperforms baselines on Paper2CodeBench, improving reference-based fidelity by 13.8% and halving high-severity evaluator critiques.
PaperCompiler is a framework that translates research papers into repository-level code by compiling paper-grounded evidence into explicit implementation specifications. It preserves source provenance, distinguishes between paper-supported, inferred, externally delegated, and unresolved information, and encodes requirements such as non-degradation, ownership, cross-file dependencies, and file-level constraints. The framework improves reference-based fidelity by 13.8% and cuts high-severity evaluator critiques from 13.2% to 6.1% on Paper2CodeBench.
By Yunhao Liu, Hong Phuc Pham, Jaehong Yoon
arXiv:2608. 19799v1 Announce Type: new Abstract: Software increasingly functions as part of the scientific instrument itself, making failures in scientific code capable of compromising not only program behavior but also the evidence underlying scientific conclusions.
By Zhipeng Xu, Jiahao Lu, Yining Zheng, Yuxin Wang, Xipeng Qiu
SWE‑bench Science is a benchmark for evaluating coding agents on scientific software engineering tasks, comprising 119 tasks from 98 GitHub repositories across 20 scientific domains. The tasks are grouped into Issue‑driven, Expert‑exploratory, and Engineering‑integration paradigms, and even the best agent, Claude Code with Opus‑5 (max), achieves a pass@1 below 50%. The study identifies four common failure mechanisms—lack of scientific knowledge, misguided exploration, incomplete repair coverage, and poor generalization—and shows that providing scientific guidance can both help and hinder repair depending on its alignment with the task.
arXiv:2606. 18237v1 Announce Type: cross Abstract: Reproducing research results from papers and released code is central to scientific progress.
By Shanda Li, Qiuhong Anna Wei, Jingwu Tang, Valerie Chen, Nihar B Shah, Tim Dettmers, Yiming Yang, Ameet Talwalkar
arXiv:2608. 09072v1 Announce Type: cross Abstract: Large language model-powered coding agents are increasingly used to modify existing code repositories, for example, by adding features or fixing bugs.
By Xin Zhou, Chun Yong Chong, Kisub Kim, Yun Peng, Rui Shu, Zihan Wu, Xu Han, Guowen Yuan, Zeyang Zhuang, Jounghoon Kim, Jeongjin Ju, Seongmin Ju, Taein Yoon, David Lo
CrossAudit proposes a Git‑native, cross‑vendor audit protocol for autonomous research pipelines, ensuring each work increment is reviewed by an agent from a different vendor against a human‑written rulebook. Audit outcomes, disputes, and rulings are stored as git commits, providing a replayable, versioned supervision history. The authors implemented the protocol with GitHub Actions and Python, deployed it in a computational‑chemistry pipeline, and conducted a seeded‑defect trial that revealed differing interpretations of the same rulebook by two vendors.
By Zhaohe Dong, Yuhao Chen
SA-Bench is a diagnostic benchmark that evaluates how well large language model (LLM) agents reproduce scientific papers by measuring semantic alignment between generated code and the papers’ specifications. It covers 30 recent machine‑learning papers, decomposing each into 1,491 Semantic Alignment Units (SAUs) that assess numerical, methodological, protocol, and ordering fidelity. Across 12 generator configurations, the best model (Claude+PaperCoder) scores only 0.301 on average, highlighting that current LLMs often implement requirements incorrectly or with stubs.
By Xue Hu, Zewei Pan, Zeli Su, Zhou Liu, Wentao Zhang
arXiv:2607. 02931v1 Announce Type: new Abstract: AI tools are accelerating scientific publication while the systems that review it struggle to keep up, and independent verification of published research has become both harder and more important.
By Haokun Liu, Filbert Aurelian Tjiaranata, Chenhao Tan
arXiv:2606. 27386v1 Announce Type: cross Abstract: Scientific publication is still organized primarily around static manuscripts, even though much of scientific progress depends on tacit know-how: how to run code, reproduce figures, interpret edge cases, choose useful follow-up directions, and avoid failed paths.
By Sirui Lu, Xiao-Liang Qi
IdeaAMBIG is a benchmark that evaluates how well research‑idea specifications are ready for implementation. It contains 660 evidence‑grounded instances, including 163 real‑world gaps from reproducibility reports and GitHub issues and 497 synthetic gaps added to codification‑ready references. The benchmark tests three capabilities—codification‑readiness assessment, defect localization, and clarification action generation—across 13 large language models, revealing that defect localization is the main bottleneck while clarification given the defect performs much better.
By Yiling Ma, Yilun Zhao, Sihong Wu, Manasi Patwardhan, Arman Cohan
arXiv:2607. 08981v1 Announce Type: cross Abstract: LLM-generated code often compiles, passes tests, and appears correct, yet breaks once deployed.
By Viraaji Mothukuri, Reza M. Parizi