EvoIn is an agent fine-tuning framework that bridges evolution and internalization, and consistently enables agents to learn stronger decision-making procedures, raising the pass rate by 10.9 points in-domain and by 9.2 points out-of-domain.
Shi-Han Dou, Shao-Hua Liu, Zhong-Hang Lu et al.· 0 citations
ExplorationBench turns the wicked problem of evaluating scientific exploration into a concrete and tractable framework built on verifiable Alien Worlds, and finds that the strongest systems can acquire and apply unfamiliar rules, while performance varies substantially across trajectories and continued exploration can s...
Ming Zhang, Zhen Xiang, Pei-Zhong Gao et al.· 0 citations
Large language models (LLMs) are increasingly deployed as mobile assistants, where a key challenge is leveraging personal information scattered across multiple applications (apps) to complete user instructions. However, due to the lack of dedicated benchmarks, their capabilities remain poorly understood. To address thi...
Junjie Ye, Zhuohui Sheng, Shao-Hua Liu et al.· 0 citations
A critique-in-the-loop self-improvement method that incorporates critique-based supervision into the actor’s self-training process and improves the actor’s exploration efficiency and solution diversity, especially on challenging queries, leading to a stronger actor model.
Zhi-Heng Xi, Dingwen Yang, Jixuan Huang et al.· Proceedings of the Thirty-Fi...· 0 citations
A novel textual representation of fault trees is proposed, and a benchmark for multi-turn dialogue systems that emphasizes robust interaction in complex environments is constructed, evaluating a model's ability to assist in malfunction localization.
Yuhui Wang, Zhi-Xiong Yang, Ming Zhang et al.· arXiv.org· 0 citations
Pera describes a persistent agent organized around perception and control components that continually perceive service-relevant signals from episodic task executions, internal context, and changes in the surrounding environment, and use these signals to construct lifecycle tasks.
Shi-Han Dou, Haoxiang Jia, Shichun Liu et al.· 1 citation
Results show that function-aware memory arbitration enables accessible information to guide actions more effectively, and improves post-failure recovery and reduces failed-action repetition and state-action recurrence.
Jiajun Dong, Yutao Hu, Fengrui Fan et al.· 0 citations
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