CoEvoke is introduced, a cross-domain framework that evokes public interactions to evidence, decisions, and task-native outcomes during trajectory collection that supports designing collection conditions to obtain corrective supervision that improves collaborative decisions with frozen solving agents.
Lin-Na Wu, Shun-Yu Yao, Yu-Heng Wang et al.· 0 citations
Large language model agents can improve across tasks by retaining reusable skills distilled from prior interactions. Recent work jointly optimizes task execution and skill extraction, enabling the policy and skillbank to co-evolve. However, as the actor continues learning, rewarding skill proposals through their reuse...
Yi-Fei Lu, Cheng Liu, Dian-Zhi Yu et al.· 0 citations
This work argues that closing this gap requires alignment at every level of the pipeline, including execution, supervision, optimization, and evaluation, rather than scale alone, and presents Wuying-Browser-Agent, a unified framework that addresses each of these levels.
A. Chen, Yan Cheng, Zhangye Han et al.· 1 citation
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