The paper audits two single‑investigator AI‑assisted research repositories to assess how well they record provenance of AI tool use. One repository contains a prescriptive trace of rule files, with 85.6% of committed lines still visible, while the other holds a narrative trace of an agent’s scratchpad, showing only 21.8% visibility. The study finds that repository‑level indicators such as the Co‑Authored‑By trailer are inconsistent and unreliable for measuring AI involvement, and it outlines which provenance claims each trace type can support or fail to support.
By Koutian Wu, Zesen Huang, Hejia Geng
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
The paper introduces an author‑in‑the‑loop collaborative audit system for reproducibility checks during peer review, where authors explicitly declare reproduction instructions via a structured checklist. An AI agent uses these declarations to investigate and assess reproducibility, while human reviewers approve requests and decide on issue resolution. The system was deployed on 36 conference papers, identifying 20 issues that were subsequently resolved, and the study examined how different checklist conditions affected the agent’s assessment.
By Yukun Dong, Linjun Zhang, Tian Zheng
arXiv:2610.39468v1 Announce Type: cross
Abstract: AI coding agents now do long, multi-step and multi-session software work with little supervision. When a developer comes back to that work, the usefu...
By Mohammed Nayeem Khan, K S Sarun Kumar
arXiv:2606. 10241v1 Announce Type: new Abstract: Autonomous improvement loops are hard to trust because the improvement process is usually external scaffolding bolted onto the agent: failures go unlogged, diagnoses cannot be replayed, and promote-or-discard decisions land in a side database rather than the agent's own history.
By Yohei Nakajima
arXiv:2608. 05179v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly used across the scientific research lifecycle: ideation, literature search, experiment design and execution, analysis, manuscript drafting, and review.
By Tianyu Ding, Aditya Nannapaneni, Bingfan Liu, Ling Zhang