AI RESEARCH
Evaluate-as-Action: Self-Evaluated Process Rewards for Retrieval-Augmented Agents
arXiv CS.AI
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ArXi:2603.09203v1 Announce Type: new Retrieval-augmented agents can query external evidence, yet their reliability in multi-step reasoning remains limited: noisy retrieval may derail multi-hop question answering, while outcome-only reinforcement learning provides credit signals that are too coarse to optimize intermediate steps.