MindSearch: Mimicking Human Minds Elicits Deep AI Searcher

Information seeking and integration is a complex cognitive task that consumes enormous time and effort. Search engines reshape the way of seeking information but often fail to align with complex human intentions. Inspired by the remarkable progress of Large Language Models (LLMs), recent works attempt to solve the information-seeking and integration 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 (思•索) 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 consisting of WebPlanner and WebSearcher. 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. Based on either GPT-4o or InternLM2.5-7B models, MindSearch demonstrates significant improvement in the response quality in terms of depth and breadth, on both closed-set and open-set QA problems. Besides, responses from MindSearch based on InternLM2.5-7B are preferable by humans to ChatGPT-Web (by GPT-4o) and Perplexity.ai applications, which implies that MindSearch with open-source models can already deliver a competitive solution to the proprietary AI search engine. Code is available at https://github.com/InternLM/MindSearch .