arXiv:2605. 09192v2 Announce Type: replace Abstract: Agent skills can remarkably improve task success rates by using human-written procedural documents, but their quality is difficult to assess without environment-grounded verification.
By Yang Zhou, Zihan Dong, Zhenting Wang, Can Jin, Shiyu Zhao, Bangwei Guo, Difei Gu, Linjun Zhang, Mu Zhou, Dimitris N. Metaxas
UniToolCall introduces a unified framework for tool-use in large language model agents, standardizing toolset construction, dataset generation, and evaluation. The framework aggregates over 22,000 tools and creates a hybrid training corpus of more than 390,000 instances by combining ten public datasets with synthetically generated, structurally controlled trajectories. It models diverse interaction patterns—single‑hop vs. multi‑hop, single‑turn vs. multi‑turn, serial vs. parallel execution—and adds an Anchor Linkage mechanism to enforce cross‑turn dependencies, while converting seven public benchmarks into a common Query–Action–Observation–Answer format for fine‑grained evaluation.
By Yijuan Liang, Xinghao Chen, Yifan Ge, Ziyi Wu, Hao Wu, Changyu Zeng, Wei Xing, Xiaoyu Shen
arXiv:2610.33907v1 Announce Type: new
Abstract: Computer-using agents are increasingly capable of performing complex tasks in the digital world. However, high-quality interaction trajectories remain...
By Zhoumianze Liu, Zichen Ding, Bowen Yang, Zehao Li, Zhenyu Wu, Yibo Zhao, Kanzhi Chen, Yian Wang, Yanan Sun, Qiushi Sun, Yu Qiao
arXiv:2609.05837v1 Announce Type: new
Abstract: LLM-based agents are increasingly deployed in real-world applications through tool-use APIs, yet training them for specific environments remains fundam...
By Zhiyi Lyu, Yewen Li, Longtao Zheng, Shengtian Yang, Lang Feng, Lei Feng, Peng Jiang, Kun Gai, Qingpeng Cai, Bo An
AgentHorizon is a benchmark comprising 1,373 computer‑use tasks with instruction‑trajectory pairs collected from 166 hours of human‑recorded activity across three operating systems. The benchmark uses a paired design that swaps instructions to create negative tasks, testing judges’ ability to distinguish successful trajectories from those that completed a similar but incompatible request. Eleven judges were evaluated, with GPT‑5.5 achieving the highest balanced accuracy of 80.9% on the full trajectory subset, while tool‑use benefits some models but harms others, revealing large variability in judges’ performance.
By Xing Han L\`u, Dheeraj Vattikonda, Sina Hajimiri, Fatemeh Pesaran Zadeh, Parishad BehnamGhader, Ghazwa Darwiche, Amirhossein Kazemnejad, Christopher Pal, Alexandre Drouin, Siva Reddy
LiteGUI is a new framework for training lightweight GUI agents that tackles the long‑horizon, multi‑path nature of GUI interactions. It introduces a two‑stage post‑training pipeline: Guided On‑Policy Distillation, which uses human‑verified multi‑solution action annotations to provide privileged teacher guidance at each state, and Multi‑Solution Dual‑Level GRPO, which combines action‑level supervision with history‑conditioned planning‑quality supervision while handling multiple valid actions. The authors also present a scalable data generation pipeline and the Lite‑Dataset, enabling extensive evaluation across GUI benchmarks and demonstrating substantial performance gains over existing training paradigms.
By Yubin Wu, Zicheng Cai, Liping Ning, Hua Wang, Zhi Chen, Yaohua Tang, Hao Chen