The paper introduces a disclosure‑gated user simulator for companion‑agent evaluation, where information release is conditioned on the agent’s behavior through a five‑level gate system. The authors train the simulator on synthetic and real data, audit its performance, and demonstrate that it preserves ranking order and score stability across 12 tested systems. Their released simulator achieves a 0.993 correlation with the original benchmark’s simulator and outperforms prompting a frontier model, which only shifts scores upward without affecting rankings.
By Yao Liu, Yu He
Using a large language model to play the user is now standard in scalable evaluation. It has a repeatedly diagnosed failure: the simulated user is excessively cooperative, so a system under test can s...
GAUGE is a new offline protocol that evaluates whether the common practice of using an LLM-as-a-judge to rank task‑oriented agents actually aligns with a verifiable reward. Across 25 agents from six providers on two benchmarks, GAUGE finds that user satisfaction scores are largely uncorrelated with task success, and that the judge’s ranking loses precision when agents are closely matched in performance. The study highlights a gap between ranking validity and construct validity in current evaluation practices.
By Umesh Bodhwani, Thanh Tran, Kai Wei
arXiv:2606. 29957v1 Announce Type: cross Abstract: Most coding-agent benchmarks are static: an agent receives a complete task description up front and is judged only by its final code.
By Yifan Wu, Zhuokai Zhao, Songlin Li, Ho Hin Lee, Jiacheng Zhu, Shirley Wu, Tianhe Yu, Serena Li, Lizhu Zhang, Xiangjun Fan, Shengzhi Li
arXiv:2607. 02975v1 Announce Type: new Abstract: Effective agency in social environments depends on when an agent seeks knowledge, when it acts, and whether its actions are justified by acquired information.
By Dan C. Hsu, Luke Lu
arXiv:2607. 21635v1 Announce Type: new Abstract: Personal agents maintain memories, learned skills, tool configurations, and policy state that evolve with each user.
By Pin Qian, Su Wang, Yihang Chen, Qiaolin Yu, Xiaoyuan Wang, Zhitong Guo, Zhicheng Wang, Junxian You
The paper proposes shifting AI agent training from isolated task completion to collaborative interaction, defining three key dimensions—Productivity, Proactivity, and Personalization (PPP). It introduces UserVille, an environment with LLM-based user simulators and user-centric feedback, and a multi-objective reinforcement learning framework that optimizes PPP using rewards from task outcomes, question effort, and preference adherence. Experiments on SWE-Bench and BrowseComp-Plus show PPP-trained agents outperform strong LLM baselines, ask more targeted questions, and generalize to unseen preferences and tasks, with a user study underscoring the value of user-centric feedback for effective, supervised collaboration.
By Weiwei Sun, Xuhui Zhou, Weihua Du, Xingyao Wang, Sean Welleck, Graham Neubig, Maarten Sap, Yiming Yang
S3Gym is an interactive benchmark designed to evaluate large language models (LLMs) on their ability to self-improve through self-testing, self-judging, and self-improvement. It separates permissive exploration from strict held-out evaluation across seven text-based games with executable environment verifiers. Experiments show that self-improvement varies by task, with different experience incorporation pathways (direct history, summary memory, or parameter training) yielding mixed results and highlighting the need for agents to transform feedback into executable, transferable policies.
By Jiajun Shi, Siyuan Tao, Yuhao Wu, Zexuan Wang, Jingyuan Zhang, Jiaheng Liu, Xinping Lei, Xinrong Zhang, Siyuan Fang, Zhewen Tan, Tianle Cai, Junhao Fang, Jiameng Huang, Yueyang Wang, Jinkai Liu, Yuxuan Zhang, Jian Yang, Zhoujun Li, Shen Yan, Wenhao Huang, Ge Zhang
arXiv:2606. 29537v2 Announce Type: replace Abstract: Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limitations of frontier agents.
By Mengqi Yuan, Zilong Zhou, Xinzhuang Xiong, Weiming Wu, Jiayang Sun, Jiamin Song, Kaiqian Cui, Bowen Wang, Haoyuan Wu, Yitong Li, Dunjie Lu, Haikong Lu, Qi Zhen, Xinyuan Wang, Jiaqi Deng, Yuhao Yang, Cheng Chen, Boyuan Zheng, Alex Su, Xiao Yu, Hao Zou, Saaket Agashe, Xing Han Lu, Manpreet Kaur, Zhengyang Qi, Vincent Sunn Chen, Frederic Sala, Dayiheng Liu, Junyang Lin, Zhou Yu, Yu Su, Siva Reddy, Xin Eric Wang, Peng Qi, Tianbao Xie, Tao Yu
arXiv:2606. 29537v1 Announce Type: new Abstract: Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limitations of frontier agents.
By Mengqi Yuan, Zilong Zhou, Xinzhuang Xiong, Weiming Wu, Jiayang Sun, Jiamin Song, Kaiqian Cui, Bowen Wang, Haoyuan Wu, Yitong Li, Dunjie Lu, Haikong Lu, Qi Zhen, Xinyuan Wang, Jiaqi Deng, Yuhao Yang, Cheng Chen, Boyuan Zheng, Alex Su, Xiao Yu, Hao Zou, Saaket Agashe, Xing Han Lu, Manpreet Kaur, Zhengyang Qi, Vincent Sunn Chen, Frederic Sala, Dayiheng Liu, Junyang Lin, Zhou Yu, Yu Su, Siva Reddy, Xin Eric Wang, Peng Qi, Tianbao Xie, Tao Yu
arXiv:2605. 22240v2 Announce Type: replace Abstract: Proactive task-oriented dialogue (TOD), such as outbound sales, demands a persuasive agent that actively probes the user's concerns and steers the conversation toward acceptance within a bounded number of turns.
By Azure Zhang, Ning Gao, Yuqin Dai, Ruiyuan Wu, Jinpeng Wang, Rena Wei Gao, Bingdong Tan, Shuzheng Gao, Zongjie Li, Chaozheng Wang
arXiv:2609.06059v1 Announce Type: new
Abstract: As large language models evolve from question-answering systems into general-purpose agents, evaluation must move beyond static answer correctness to a...
By Yu Liu, Zhilin Liu, Zhiwei Yang, Shaojie Zhang, Zheyuan Deng, Tingwei Huang, Zhenbo Luo, Lei Jiang, Yanbing Liu, Pei Fu