PatEx-Gym is a new framework for developing and evaluating agents that perform patent examination. It includes PatEx-Bench, a benchmark of 1,000 realistic tasks with large retrieval corpora, and PASS, an annotation‑free method for synthesizing search trajectories. A 4B‑parameter PatEx‑Agent trained on these synthetic trajectories provides a lightweight baseline that competes with much larger general‑purpose models, yet prior‑art discovery remains challenging.
By Qiyao Wang, Xinyi Chen, Hongbo Wang, Jiayuan Lu, Jianhang Li, Minghuan Tan, Yuan Lin, Min Yang
arXiv:2608.21249v1 Announce Type: new
Abstract: While recent large language models (LLMs) have achieved promising results on individual patent drafting tasks, they fundamentally fail to investigate t...
By Lekang Jiang, Wenjun Sun, Stephan Goetz
arXiv:2609.13422v1 Announce Type: new
Abstract: LLM judges are increasingly used to evaluate and improve AI-generated outputs, yet their reliability for complex professional work remains unclear. We...
By Toshiaki Koike-Akino, Vlad Blaykhman, Ye Wang, Jing Liu, Gene V. Vinokur
arXiv:2607. 28229v1 Announce Type: cross Abstract: The web is increasingly accessed by AI agents rather than humans.
By Luigi Sigillo, Matteo Silvestri, Francesco Tabaro, Rajat Bhatnagar, Syed Irtaza Mubashar, Matt Jeffryes, Daljit Nijjer, Vittorio Perera, Ola Spjuth, Julio Saez-Rodriguez, Melissa Harrison, Fabio Petroni
arXiv:2610.32441v1 Announce Type: new
Abstract: Large language model (LLM) agents are increasingly capable of supporting scientific workflows, but their capabilities across diverse research activitie...
By Tianyun Zhong, Wei Wang, Jialin Li, Jianhong Tu, Liyao Li, Chuqiao Kuang, Tianshi Zheng, Songchi Zhou, Weiqi Zhai, Qile Jin, Jiajia Li, Jiasheng Zheng, Boxi Cao, Yaojie Lu, Hongyu Lin, Shiwei Ye, Bing Zhao, Hu Wei, Lin Qu, Xianpei Han
StartupBench is a benchmark that evaluates general‑purpose agents on end‑to‑end workflows derived from real AI startup products that have proven market adoption. It translates these product workflows into deliverable‑oriented tasks and assesses them with detailed rubrics that capture complex requirements. Even the best current models complete only about 30% of the tasks, highlighting failures in instruction following and domain expertise.