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Institute of Software, Chinese Academy of Sciences
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Joint Policy and Prompt Optimization (P O) is introduced to mitigate advantage collapse by alternating continuous policy updates with discrete prompt evolution, providing a self-reinforcing framework that facilitates more effective LLM alignment.
DocOps is introduced, a deterministically verifiable evaluation framework underpinned by a hierarchical taxonomy that deconstructs document operations inspired by real-world practices into atomic dimensions and escalating workflow complexities that exposes the capability boundaries of agents in maintaining global docum...
Experimental results show that RUPA consistently outperforms existing UQ methods by providing more accurate uncertainty estimates, enabling earlier failure detection, and improving uncertainty-guided agent execution across diverse agent tasks.
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