Critic-R: Improving Agentic Search using Instruction-tuned Retrievers with Natural Language Introspective Feedback 事件

Critic-R: Improving Agentic Search using Instruction-tuned Retrievers with Natural Language Introspective Feedback arXiv:2606.00590v1 Announce Type: cross Abstract: Agentic search systems iteratively interact with retrieval models to answer complex queries. Despite substantial progress, optimizing retrievers for agentic search remains challenging, often requiring heavy co-training or gold-standard annotations that limit real-world applicability. We propose Critic-R, a framework that explicitly