| title | Critic-R: Improving Agentic Search using Instruction-tuned Retrievers with Natural Language Introspective Feedback |
|---|---|
| authors | Md Zarif Ul Alam, Alireza Salemi, Hamed Zamani |
| date | 2026-05-30 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.00590 |
| affiliation | University of Massachusetts Amherst |
| upvotes | 1 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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 closes the feedback loop between the reasoning agent and the retrieval model during both inference and training. Critic-R introduces a critic model that evaluates the agent's introspective reasoning trace after consuming retrieved evidence to determine whether the retrieved context sufficiently supports the next reasoning step. Critic-R has two complementary mechanisms: Critic-R-Zero, an inference-time query refinement loop that iteratively rewrites queries and retrieval instructions, and Critic-Embed, an optimization approach for retrieval models that leverages successful and failed refinement trajectories as automatic supervision without requiring manual relevance annotation. We evaluate Critic-R on HotpotQA, 2WikiMultihopQA, MuSiQue, and Bamboogle. Results show that Critic-R significantly improves both retrieval quality and downstream answer accuracy. |
cite
@misc{alam2026critic,
title={Critic-R: Improving Agentic Search using Instruction-tuned Retrievers with Natural Language Introspective Feedback},
author={Md Zarif Ul Alam and Alireza Salemi and Hamed Zamani},
year={2026},
eprint={2606.00590},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.00590}
}