Language model agents are increasingly deployed in open-world tool environments, which require balancing exploring unknown capabilities and exploiting known ones. Existing methods face a performance-efficiency tradeoff: they either rigidly decouple exploration and execution or interleave them without coordination. We a...
Shengbin Yue, Hongru Wang, Siyuan Wang et al.· 0 citations
This survey provides a comprehensive analysis of reasoning economy in both the post-training and test-time inference stages of LLMs, encompassing the cause of reasoning inefficiency, behavior analysis of different reasoning patterns, and potential solutions to achieve reasoning economy.
A new task, Behavior-Aware Travel Planning, which infers user preferences directly from past behaviors and generates personalized travel plans and proposes B2T-Agent, a reinforcement learning-based agent that leverages user behavior trajectories, interacts with external tools for preference-aligned retrieval, and maint...
Zihao Cheng, Yingyu Shan, Hongru Wang et al.· 1 citation
CAP is introduced, a scalable benchmark for evaluating browser agents on cross-site, human-like web tasks that require non-trivial UI interactions and visual understanding and a decomposition-and-recomposition pipeline that first abstracts each website into a structured site card capturing user-facing functions, comple...
Zejun Xu, Taiyi Chen, Jin Li et al.· 0 citations
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