Long-document visual question answering increasingly relies on VLM agents that retrieve candidate pages, inspect page images, write findings to working memory, and synthesize answers. Working memory should carry answer-supporting evidence across page inspections for later grounded answering, yet existing evaluation mai...
Dongzhuoran Zhou, Yuqicheng Zhu, Yu-Le Liu et al.· 3 citations· ⚡1
This work introduces ScaleCUA, a unified framework that scales online RL for CUAs via verifiable task synthesis and efficient training, and designs VeriGen, an end-to-end framework for generating verifiable RL tasks through iterative docker interactions and a multi-agent feedback loop.
This work introduces KARL (Knowledge-Augmented Reinforcement Learning), a framework that enables LLM agents to dynamically explore structured knowledge sources through multi-turn interactions, and empowers agents to proactively decide when and what knowledge to acquire during task execution.
Xueqiao Sun, Xiao Liu, Bowen Lv et al.· Annual Meeting of the Associ...· 0 citations
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