QAgent: A modular Search Agent with Interactive Query Understanding

• URL: http://arxiv.org/abs/2510.08383v1
• Date: Thu, 09 Oct 2025 16:08:05 GMT
• Title: QAgent: A modular Search Agent with Interactive Query Understanding
• Authors: Yi Jiang, Lei Shen, Lujie Niu, Sendong Zhao, Wenbo Su, Bo Zheng,
• Abstract summary: Large language models excel at natural language tasks but are limited by their static parametric knowledge.<n>We propose a unified agentic RAG framework that employs a search agent for adaptive retrieval.<n> Experiments show QAgent excels at QA and serves as a plug-and-play module for real-world deployment.
• Score: 25.147900132089777
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) excel at natural language tasks but are limited by their static parametric knowledge, especially in knowledge-intensive task. Retrieval-augmented generation (RAG) mitigates this by integrating external information. However, (1) traditional RAG struggles with complex query understanding, and (2) even search agents trained with reinforcement learning (RL), despite their promise, still face generalization and deployment challenges. To address these limitations, we propose QAgent, a unified agentic RAG framework that employs a search agent for adaptive retrieval. This agent optimizes its understanding of the query through interactive reasoning and retrieval. To facilitate real-world application, we focus on modular search agent for query understanding that are plug-and-play in complex systems. Secifically, the agent follows a multi-step decision process trained with RL to maximize retrieval quality and support accurate downstream answers. We further analyze the strengths and weaknesses of end-to-end RL and propose a strategy that focuses on effective retrieval, thereby enhancing generalization in LLM applications. Experiments show QAgent excels at QA and serves as a plug-and-play module for real-world deployment.

Related papers

• AgentIF-OneDay: A Task-level Instruction-Following Benchmark for General AI Agents in Daily Scenarios [49.90735676070039]
  The capacity of AI agents to effectively handle tasks of increasing duration and complexity continues to grow.<n>We argue that current evaluations prioritize increasing task difficulty without sufficiently addressing the diversity of agentic tasks.<n>We propose AgentIF-OneDay, aimed at determining whether general users can utilize natural language instructions and AI agents to complete a diverse array of daily tasks.
  arXiv  Detail & Related papers  (2026-01-28T13:49:18Z)
• Beyond Monolithic Architectures: A Multi-Agent Search and Knowledge Optimization Framework for Agentic Search [56.78490647843876]
  Agentic search has emerged as a promising paradigm for complex information seeking by enabling Large Language Models (LLMs) to interleave reasoning with tool use.<n>We propose bfM-ASK, a framework that explicitly decouples agentic search into two complementary roles: Search Behavior Agents, which plan and execute search actions, and Knowledge Management Agents, which aggregate, filter, and maintain a compact internal context.
  arXiv  Detail & Related papers  (2026-01-08T08:13:27Z)
• Interact-RAG: Reason and Interact with the Corpus, Beyond Black-Box Retrieval [49.85856484781787]
  We introduce Interact-RAG, a new paradigm that elevates the LLM agent into an active manipulator of the retrieval process.<n>We develop a reasoning-enhanced workflow, which enables both zero-shot execution and the synthesis of interaction trajectories.<n>Experiments across six benchmarks demonstrate that Interact-RAG significantly outperforms other advanced methods.
  arXiv  Detail & Related papers  (2025-10-31T15:48:43Z)
• Empowering RepoQA-Agent based on Reinforcement Learning Driven by Monte-carlo Tree Search [70.63903518295785]
  We introduce RepoSearch-R1, a novel agentic reinforcement learning framework driven by Monte-carlo Tree Search.<n>Based on RepoSearch-R1, we construct a RepoQA-Agent specifically designed for repository question-answering tasks.
  arXiv  Detail & Related papers  (2025-10-30T09:10:36Z)
• A Comprehensive Survey on Reinforcement Learning-based Agentic Search: Foundations, Roles, Optimizations, Evaluations, and Applications [41.610769640632334]
  Retrieval-Augmented Generation (RAG) mitigates issues by grounding model outputs in external evidence.<n>Recent advances in agentic search address these limitations by enabling LLMs to plan, retrieve, and reflect through multi-step interaction with search environments.<n>Within this paradigm, reinforcement learning (RL) offers a powerful mechanism for adaptive and self-improving search behavior.
  arXiv  Detail & Related papers  (2025-10-19T06:04:53Z)
• AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering [51.07491603393163]
  tAgent is a framework that formulates multi-agent QA as a knowledge-graph-guided routing problem supervised by empirical performance signals.<n>By leveraging soft supervision and weighted aggregation of agent outputs, Agent learns principled collaboration schemes that capture the complementary strengths of diverse agents.
  arXiv  Detail & Related papers  (2025-10-06T23:20:49Z)
• WebSailor-V2: Bridging the Chasm to Proprietary Agents via Synthetic Data and Scalable Reinforcement Learning [73.91893534088798]
  WebSailor is a complete post-training methodology designed to instill this crucial capability.<n>Our approach involves generating novel, high-uncertainty tasks through structured sampling and information obfuscation.<n>WebSailor significantly outperforms all open-source agents in complex information-seeking tasks.
  arXiv  Detail & Related papers  (2025-09-16T17:57:03Z)
• 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)
• WebSailor: Navigating Super-human Reasoning for Web Agent [72.5231321118689]
  WebSailor is a complete post-training methodology designed to instill this crucial capability.<n>Our approach involves generating novel, high-uncertainty tasks through structured sampling and information obfuscation.<n>WebSailor significantly outperforms all opensource agents in complex information-seeking tasks.
  arXiv  Detail & Related papers  (2025-07-03T12:59:07Z)
• Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG [0.8463972278020965]
  Large Language Models (LLMs) have revolutionized artificial intelligence (AI) by enabling human like text generation and natural language understanding.<n>Retrieval Augmented Generation (RAG) has emerged as a solution, enhancing LLMs by integrating real time data retrieval to provide contextually relevant responses.<n>Agentic Retrieval-Augmented Generation (RAG) transcends these limitations by embedding autonomous AI agents into the RAG pipeline.
  arXiv  Detail & Related papers  (2025-01-15T20:40:25Z)
• Talk to Right Specialists: Routing and Planning in Multi-agent System for Question Answering [47.29580414645626]
  RopMura is a novel multi-agent system that integrates multiple knowledge bases into a unified RAG-based agent.<n>RopMura features two key components: a router that intelligently selects the most relevant agents based on knowledge boundaries and a planner that decomposes complex multi-hop queries into manageable steps.
  arXiv  Detail & Related papers  (2025-01-14T03:25:26Z)
• RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent [15.836845304125436]
  RS-Agent is an AI agent designed to interact with human users and autonomously leverage specialized models.<n> RS-Agent integrates four key components: a Central Controller based on large language models, a dynamic toolkit for tool execution, a Solution Space for task-specific expert guidance, and a Knowledge Space for domain-level reasoning.<n>Extensive experiments across 9 datasets and 18 remote sensing tasks demonstrate that RS-Agent significantly outperforms state-of-the-art MLLMs.
  arXiv  Detail & Related papers  (2024-06-11T09:30:02Z)

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.