Scaling Retrieval-Augmented Reasoning with Parallel Search and Explicit Merging

• URL: http://arxiv.org/abs/2605.13534v1
• Date: Wed, 13 May 2026 13:46:35 GMT
• Title: Scaling Retrieval-Augmented Reasoning with Parallel Search and Explicit Merging
• Abstract summary: MultiSearch is an RL-based framework that addresses limitations through multi-query retrieval and explicit merging of retrieved information.<n>At each reasoning step, MultiSearch generates queries from multiple perspectives and retrieves external information in parallel, expanding the scope of relevant information.<n>Then, the agent consolidates and refines retrieved information at the merging process, improving the SNR and ensuring more accurate reasoning.
• Score: 14.434955701466597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 information in parallel, expanding the scope of relevant information and mitigating the reliance on any single retrieval result. Then, the agent consolidates and refines retrieved information at the merging process, improving the SNR and ensuring more accurate reasoning. Additionally, we propose a reinforcement learning framework with a multi-process reward design to optimize agents for both multi-query retrieval and information consolidation. Extensive experiments on seven benchmarks demonstrate that MultiSearch outperforms baseline methods, enhancing the SNR of retrieval and improving reasoning performance in question-answering tasks.

Related papers

• Mitigating Context Interference for Reliable and Efficient Search Agents [67.82705889007238]
  This study investigates context interference in multi-turn search agents.<n>We introduce a distill-based context refiner to dynamically mitigate context interference for multi-turn search agents.<n>We validate that incorporating context refinement into RL training pipelines of search agents can significantly enhance both reliability and efficiency.
  arXiv  Detail & Related papers  (2026-08-11T10:00:42Z)
• SIGHT: Reinforcement Learning with Self-Evidence and Information-Gain Diverse Branching for Search Agent [39.43590030917357]
  SIGHT is a framework that enhances search-based reasoning through Self-Evidence Support and Information-Gain Driven Diverse Branching.<n>SIGHT distills search results into high-fidelity evidence via SES and calculates an Information Gain score to pinpoint pivotal states.<n> Experiments on single-hop and multi-hop QA benchmarks demonstrate that SIGHT significantly outperforms existing approaches.
  arXiv  Detail & Related papers  (2026-02-12T04:16:55Z)
• Beyond the limitation of a single query: Train your LLM for query expansion with Reinforcement Learning [23.104182075898297]
  Reasoning-augmented search agents, such as Search-R1, are trained to reason, search, and generate the final answer iteratively.<n>We train an LLM-based search agent with the native capability of query expansion through reinforcement learning.<n>With the assistance of the squeezer model, we discover that even a small-scale 3B LLM can demonstrate a strong capability of query expansion.
  arXiv  Detail & Related papers  (2025-10-11T04:23:30Z)
• MSRS: Evaluating Multi-Source Retrieval-Augmented Generation [51.717139132190574]
  Many real-world applications demand the ability to integrate and summarize information scattered across multiple sources.<n>We present a scalable framework for constructing evaluation benchmarks that challenge RAG systems to integrate information across distinct sources.
  arXiv  Detail & Related papers  (2025-08-28T14:59:55Z)
• Hybrid Deep Searcher: Integrating Parallel and Sequential Search Reasoning [57.78245296980122]
  We introduce HDS-QA (Hybrid Deep Search QA), a dataset automatically generated from Natural Questions.<n>It comprises hybrid-hop questions that combine parallelizable independent subqueries (executable simultaneously) and sequentially dependent subqueries (requiring step-by-step resolution)<n>We name the model HybridDeepSearcher, which outperforms state-of-the-art baselines across multiple benchmarks.
  arXiv  Detail & Related papers  (2025-08-26T15:15:17Z)
• DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router [57.28685457991806]
  DeepSieve is an agentic RAG framework that incorporates information sieving via LLM-as-a-knowledge-router.<n>Our design emphasizes modularity, transparency, and adaptability, leveraging recent advances in agentic system design.
  arXiv  Detail & Related papers  (2025-07-29T17:55:23Z)
• DynaSearcher: Dynamic Knowledge Graph Augmented Search Agent via Multi-Reward Reinforcement Learning [5.280613615397194]
  DynaSearcher is an innovative search agent enhanced by dynamic knowledge graphs and multi-reward reinforcement learning (RL)<n>We employ a multi-reward RL framework for fine-grained control over training objectives such as retrieval accuracy, efficiency, and response quality.<n> Experimental results demonstrate that our approach achieves state-of-the-art answer accuracy on six multi-hop question answering datasets.
  arXiv  Detail & Related papers  (2025-07-23T09:58:31Z)
• MMSearch-R1: Incentivizing LMMs to Search [49.889749277236376]
  We present MMSearch-R1, the first end-to-end reinforcement learning framework that enables on-demand, multi-turn search in real-world Internet environments.<n>Our framework integrates both image and text search tools, allowing the model to reason about when and how to invoke them guided by an outcome-based reward with a search penalty.
  arXiv  Detail & Related papers  (2025-06-25T17:59:42Z)
• R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning [0.8388591755871735]
  R-Search is a reinforcement learning framework for Reasoning-Search integration.<n>It guides large language models to autonomously execute multi-step reasoning with deep search interaction.<n>R-Search learns optimal reasoning search interaction trajectories via multi-reward signals.
  arXiv  Detail & Related papers  (2025-06-04T17:29:22Z)
• Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers [74.17516978246152]
  Large language models (LLMs) have been widely integrated into information retrieval to advance traditional techniques.<n>We propose EXSEARCH, an agentic search framework, where the LLM learns to retrieve useful information as the reasoning unfolds.<n>Experiments on four knowledge-intensive benchmarks show that EXSEARCH substantially outperforms baselines.
  arXiv  Detail & Related papers  (2025-05-26T15:27:55Z)
• Knowledge-Aware Iterative Retrieval for Multi-Agent Systems [0.0]
  We introduce a novel large language model (LLM)-driven agent framework.<n>It iteratively refines queries and filters contextual evidence by leveraging dynamically evolving knowledge.<n>The proposed system supports both competitive and collaborative sharing of updated context.
  arXiv  Detail & Related papers  (2025-03-17T15:27:02Z)
• MultiConIR: Towards multi-condition Information Retrieval [38.864056667809095]
  MultiConIR is a benchmark designed to evaluate retrieval and reranking models under nuanced multi-condition query scenarios.<n>Most retrievers and rerankers exhibit severe performance degradation as query complexity increases.<n>This work delves into the factors contributing to reranker performance deterioration and examines how condition positioning within queries affects similarity assessment.
  arXiv  Detail & Related papers  (2025-03-11T05:02:03Z)
• Synergistic Interplay between Search and Large Language Models for Information Retrieval [141.18083677333848]
  InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections. InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-12T11:58:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.