arXiv:2606. 18874v1 Announce Type: new Abstract: AI systems can increasingly automate scientific workflows, but the reasoning that links prior evidence, generated ideas, experiments and final claims often remains implicit inside model inference.
By Zijian Wang, Hanqi Li, Ziyue Yang, Zijian Hu, Shenghan Zuo, Yunzhe Zhang, Da Ma, Danyu Luo, Chenrun Wang, Jing Peng, Tiancheng Huang, Sijia Guo, Huayang Wang, Zichen Zhu, Senyu Han, Yilu Cao, Kai Yu, Lu Chen
arXiv:2608.28596v1 Announce Type: new
Abstract: Large language model (LLM) agents are increasingly embedded in scientific workflows for literature analysis, drafting, and review. Existing systems adv...
By Nidhi Jha, Siddharth Chaudhary, Ajinkya Kulkarni
As autonomous AI agents take on every stage of scientific inquiry, research output is expanding far beyond human review capacity. Yet scientific communication still relies on natural-language prose: a...
The paper proposes a normative framework for ethical use of large language models (LLMs) in scientific research, treating reasoning as a distributed process where human control remains essential for epistemic legitimacy. It introduces key constructs—content origin, human verification, responsibility assignment, accountable ownership, and epistemic outcome—to separate claim provenance from verification and responsibility. The authors argue that the ethical boundary hinges on adequate verification and accountable human ownership, and they propose an "epistemic audit" to document delegation, verification, provenance, and responsibility for transparent, reviewable AI-assisted reasoning.
By Kalin Stoyanov
arXiv:2607. 22511v1 Announce Type: cross Abstract: Automating theoretical research is constrained not only by the generation of candidate results, but also by their reliable evaluation.
By Jiyuan Tan, Vasilis Syrgkanis
TruthInsightBench is a new benchmark designed to evaluate automated scientific discovery agents by presenting them with 40 blind tasks drawn from peer‑reviewed studies across ten domains. Each task provides only a neutral objective and frozen data, withholding source conclusions, expected values, and analysis paths, forcing agents to determine which claim the data support. A fixed LLM‑based judge scores agents on evidentiary maturity across six dimensions, using 29 artifact‑grounded items, enabling fully automated, repeatable evaluation without human grading.
By Zhibo Yang, Chen Zhang, Yuewei Zhang, Hao Wang