From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
- URL: http://arxiv.org/abs/2506.18959v2
- Date: Thu, 26 Jun 2025 17:18:00 GMT
- Title: From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
- Authors: Weizhi Zhang, Yangning Li, Yuanchen Bei, Junyu Luo, Guancheng Wan, Liangwei Yang, Chenxuan Xie, Yuyao Yang, Wei-Chieh Huang, Chunyu Miao, Henry Peng Zou, Xiao Luo, Yusheng Zhao, Yankai Chen, Chunkit Chan, Peilin Zhou, Xinyang Zhang, Chenwei Zhang, Jingbo Shang, Ming Zhang, Yangqiu Song, Irwin King, Philip S. Yu,
- Abstract summary: Large Language Models (LLMs), endowed with reasoning and agentic capabilities, are ushering in a new paradigm termed Agentic Deep Research.<n>We trace the evolution from static web search to interactive, agent-based systems that plan, explore, and learn.<n>We demonstrate that Agentic Deep Research not only significantly outperforms existing approaches, but is also poised to become the dominant paradigm for future information seeking.
- Score: 96.65646344634524
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Information retrieval is a cornerstone of modern knowledge acquisition, enabling billions of queries each day across diverse domains. However, traditional keyword-based search engines are increasingly inadequate for handling complex, multi-step information needs. Our position is that Large Language Models (LLMs), endowed with reasoning and agentic capabilities, are ushering in a new paradigm termed Agentic Deep Research. These systems transcend conventional information search techniques by tightly integrating autonomous reasoning, iterative retrieval, and information synthesis into a dynamic feedback loop. We trace the evolution from static web search to interactive, agent-based systems that plan, explore, and learn. We also introduce a test-time scaling law to formalize the impact of computational depth on reasoning and search. Supported by benchmark results and the rise of open-source implementations, we demonstrate that Agentic Deep Research not only significantly outperforms existing approaches, but is also poised to become the dominant paradigm for future information seeking. All the related resources, including industry products, research papers, benchmark datasets, and open-source implementations, are collected for the community in https://github.com/DavidZWZ/Awesome-Deep-Research.
Related papers
- DynaSearcher: Dynamic Knowledge Graph Augmented Search Agent via Multi-Reward Reinforcement Learning [4.817888539036794]
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) - Deep Research Agents: A Systematic Examination And Roadmap [79.04813794804377]
Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
arXiv Detail & Related papers (2025-06-22T16:52:48Z) - InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation [63.55258191625131]
InfoDeepSeek is a new benchmark for assessing agentic information seeking in real-world, dynamic web environments.<n>We propose a systematic methodology for constructing challenging queries satisfying the criteria of determinacy, difficulty, and diversity.<n>We develop the first evaluation framework tailored to dynamic agentic information seeking, including fine-grained metrics about the accuracy, utility, and compactness of information seeking outcomes.
arXiv Detail & Related papers (2025-05-21T14:44:40Z) - WebThinker: Empowering Large Reasoning Models with Deep Research Capability [60.81964498221952]
WebThinker is a deep research agent that empowers large reasoning models to autonomously search the web, navigate web pages, and draft research reports during the reasoning process.<n>It also employs an textbfAutonomous Think-Search-and-Draft strategy, allowing the model to seamlessly interleave reasoning, information gathering, and report writing in real time.<n>Our approach enhances LRM reliability and applicability in complex scenarios, paving the way for more capable and versatile deep research systems.
arXiv Detail & Related papers (2025-04-30T16:25:25Z) - DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments [20.498100965239818]
We introduce DeepResearcher, the first comprehensive framework for end-to-end training of LLM-based deep research agents.<n>Unlike RAG-based approaches that assume all necessary information exists within a fixed corpus, our method trains agents to navigate the noisy, unstructured, and dynamic nature of the open web.<n>Extensive experiments on open-domain research tasks demonstrate that DeepResearcher achieves substantial improvements of up to 28.9 points over prompt engineering-based baselines.
arXiv Detail & Related papers (2025-04-04T04:41:28Z) - Toward Agentic AI: Generative Information Retrieval Inspired Intelligent Communications and Networking [87.82985288731489]
Agentic AI has emerged as a key paradigm for intelligent communications and networking.<n>This article emphasizes the role of knowledge acquisition, processing, and retrieval in agentic AI for telecom systems.
arXiv Detail & Related papers (2025-02-24T06:02:25Z) - Boosting Search Engines with Interactive Agents [25.89284695491093]
This paper presents first steps in designing agents that learn meta-strategies for contextual query refinements.
Agents are empowered with simple but effective search operators to exert fine-grained and transparent control over queries and search results.
arXiv Detail & Related papers (2021-09-01T13:11:57Z)
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.