arXiv:2606. 08036v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in academic research workflows, but scholarly tasks require high factual precision and therefore expose a key weakness: overconfidence.
By Zongrng Li, Mingzheng Yang, Lei Zou, Hongxu Ma, Hao Tian, Siqi Zhou, Wenjing Gong, Kaili Zhang, Bingqian Chen, Mitch Zhang, Yifan Yang
arXiv:2609.00747v1 Announce Type: new
Abstract: Large language models increasingly generate research ideas, yet judging their novelty or feasibility at generation time does not establish whether they...
By Fenghai Li, Zihan Tang, Haofei Yu, Yining Zhao, Jiaxuan You
arXiv:2605. 04135v2 Announce Type: replace-cross Abstract: Readers of applied-domain LLM capability evaluations want to know what AI systems can currently do.
By David Gringras, Misha Salahshoor
arXiv:2607. 01233v1 Announce Type: cross Abstract: LLMs are increasingly used to brainstorm research ideas, but existing evaluations mostly judge individual ideas by novelty, feasibility, or expert preference.
By Ziyu Chen, Yilun Zhao, Arman Cohan
arXiv:2602. 08873v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are now used for academic expert recommendation.
By Lisette Esp\'in-Noboa, Gonzalo Gabriel M\'endez
arXiv:2608.31118v1 Announce Type: new
Abstract: The effect of Large Language Model (LLM) scale on ontology learning (OL) performance remains insufficiently characterized. We present a controlled eval...
By Hamed Babaei Giglou, S\"oren Auer, Jennifer D'Souza