From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents
This addresses the problem of handling complex, multi-step information retrieval for users across diverse domains, representing a potential paradigm shift rather than an incremental improvement.
The paper tackles the inadequacy of traditional keyword-based search engines for complex information needs by proposing Agentic Deep Research, a new paradigm using reasoning agents with LLMs, which significantly outperforms existing approaches and is poised to become dominant in information seeking.
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.