title: MindSearch: Mimicking Human Minds Elicits Deep AI Searcher
summary: Information seeking and integration is a complex cognitive task that consumes
  enormous time and effort. Inspired by the remarkable progress of Large Language
  Models, recent works attempt to solve this task by combining LLMs and search
  engines. However, these methods still obtain unsatisfying performance due to
  three challenges: (1) complex requests often cannot be accurately and
  completely retrieved by the search engine once (2) corresponding information to
  be integrated is spread over multiple web pages along with massive noise, and
  (3) a large number of web pages with long contents may quickly exceed the
  maximum context length of LLMs. Inspired by the cognitive process when humans
  solve these problems, we introduce MindSearch to mimic the human minds in web
  information seeking and integration, which can be instantiated by a simple yet
  effective LLM-based multi-agent framework. The WebPlanner models the human mind
  of multi-step information seeking as a dynamic graph construction process: it
  decomposes the user query into atomic sub-questions as nodes in the graph and
  progressively extends the graph based on the search result from WebSearcher.
  Tasked with each sub-question, WebSearcher performs hierarchical information
  retrieval with search engines and collects valuable information for WebPlanner.
  The multi-agent design of MindSearch enables the whole framework to seek and
  integrate information parallelly from larger-scale (e.g., more than 300) web
  pages in 3 minutes, which is worth 3 hours of human effort. MindSearch
  demonstrates significant improvement in the response quality in terms of depth
  and breadth, on both close-set and open-set QA problems. Besides, responses
  from MindSearch based on InternLM2.5-7B are preferable by humans to ChatGPT-Web
  and Perplexity.ai applications, which implies that MindSearch can already
  deliver a competitive solution to the proprietary AI search engine.
paper_authors._id: 66a85f2f5bd5c24dec0ed484
paper_authors.name: Zehui Chen
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paper_authors.fullname: Zehui Chen
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paper_authors.user: lovesnowbest
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paper_authors.name: Kuikun Liu
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paper_authors.fullname: liu
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paper_authors._id: 66a85f2f5bd5c24dec0ed486
paper_authors.name: Qiuchen Wang
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paper_authors.fullname: QiuchenWang
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paper_authors.user: autumncc
paper_authors._id: 66a85f2f5bd5c24dec0ed487
paper_authors.name: Jiangning Liu
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paper_authors._id: 65a7646581cc3017643a217d
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paper_authors.fullname: Jiangning Liu
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paper_authors.user: liujiangning
paper_authors._id: 66a85f2f5bd5c24dec0ed488
paper_authors.name: Wenwei Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-06T07:45:38.673Z
paper_authors._id: 64e8505321540e1da3226b54
paper_authors.avatarUrl: /avatars/18958b8406d1ce492b54c1c839f18c54.svg
paper_authors.fullname: Wenwei Zhang
paper_authors.type: user
paper_authors.user: ZwwWayne
paper_authors._id: 66a85f2f5bd5c24dec0ed489
paper_authors.name: Kai Chen
paper_authors._id: 66a85f2f5bd5c24dec0ed48a
paper_authors.name: Feng Zhao
paper_title: MindSearch: Mimicking Human Minds Elicits Deep AI Searcher
paper_summary: Information seeking and integration is a complex cognitive task that consumes
  enormous time and effort. Inspired by the remarkable progress of Large Language
  Models, recent works attempt to solve this task by combining LLMs and search
  engines. However, these methods still obtain unsatisfying performance due to
  three challenges: (1) complex requests often cannot be accurately and
  completely retrieved by the search engine once (2) corresponding information to
  be integrated is spread over multiple web pages along with massive noise, and
  (3) a large number of web pages with long contents may quickly exceed the
  maximum context length of LLMs. Inspired by the cognitive process when humans
  solve these problems, we introduce MindSearch to mimic the human minds in web
  information seeking and integration, which can be instantiated by a simple yet
  effective LLM-based multi-agent framework. The WebPlanner models the human mind
  of multi-step information seeking as a dynamic graph construction process: it
  decomposes the user query into atomic sub-questions as nodes in the graph and
  progressively extends the graph based on the search result from WebSearcher.
  Tasked with each sub-question, WebSearcher performs hierarchical information
  retrieval with search engines and collects valuable information for WebPlanner.
  The multi-agent design of MindSearch enables the whole framework to seek and
  integrate information parallelly from larger-scale (e.g., more than 300) web
  pages in 3 minutes, which is worth 3 hours of human effort. MindSearch
  demonstrates significant improvement in the response quality in terms of depth
  and breadth, on both close-set and open-set QA problems. Besides, responses
  from MindSearch based on InternLM2.5-7B are preferable by humans to ChatGPT-Web
  and Perplexity.ai applications, which implies that MindSearch can already
  deliver a competitive solution to the proprietary AI search engine.
paper_ai_summary: MindSearch, an LLM-based multi-agent framework, improves web information seeking and integration through parallel processing and hierarchical retrieval, achieving better performance than existing solutions.
paper_ai_keywords: Large Language Models LLMs multi-agent framework dynamic graph construction hierarchical information retrieval WebPlanner WebSearcher close-set QA problems open-set QA problems InternLM2.5-7B ChatGPT-Web Perplexity.ai