Scaling Retrieval-Augmented Reasoning with Parallel Search and Explicit Merging
Deep search agents have proven effective in enhancing LLMs by retrieving external knowledge during multi-step reasoning. However, existing methods often generate a single query for retrieval at each reasoning step, limiting information coverage and introducing high noise. This may result in low signal-to-noise ratios (SNR) during search, degrading reasoning accuracy and leading to unnecessary reasoning steps. In this paper, we introduce MultiSearch, an RL-based framework that addresses these limitations through multi-query retrieval and explicit merging of retrieved information. At each reasoning step, MultiSearch generates queries from multiple perspectives and retrieves external informa...
| Authors | Jiabei Liu, Wenyu Mao, Junfei Tan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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