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.
ReproAgent is a four‑stage pipeline—Prepare, Plan, Generate, Repair—that uses a persistent implementation contract to guide scientific AI agents in converting research papers into executable code repositories. The system employs two channels: an implementation‑requirement channel that translates paper snippets into code obligations, and a reference‑evidence channel that pulls content and structure from related repositories. Evaluated on PaperBench Code‑Dev, ReproAgent achieves the highest mean score among same‑backbone scaffolds for both Claude‑Sonnet‑4.5 and Gemini‑3‑Flash, with ablation studies confirming the contribution of both channels.
By Xue Hu, Zewei Pan, Zhongyuan Wang, Zhou Liu, Zeli Su, Wentao Zhang
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
LLM-based agents excel at software engineering tasks where an existing codebase provides context, but constructing a program from scratch remains fundamentally harder. Recent benchmarks such as ProgramBench quantify this gap: given only natural-language documentation and an execute-only binary as a behavioral oracle, even frontier models solve fewer than 1% of instances.
arXiv:2608. 13522v1 Announce Type: cross Abstract: AI agents are increasingly used for programming, but do not provide any guarantee on the correctness of generated code.
By Zhe Ye, Hantao Lou, Yuechun Sun, Peiyang Song, Zhengxu Yan, Timothe Kasriel, Qingyang Zhang, Kaiyu Yang, Soonho Kong, Jingxuan He, Dawn Song
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