Go to ACL ARR 2025 May homepageManuSearch: Democratizing Deep Search in Large Language Models with a Transparent and Open Multi-Agent Framework
Abstract: Recent advances in web-augmented large language models (LLMs) have exhibited strong performance in complex reasoning tasks, yet these capabilities are mostly locked in proprietary systems with opaque architectures. In this work, we propose $\textbf{ManuSearch}$, a transparent and modular multi-agent framework designed to democratize deep search for LLMs. ManuSearch decomposes the search and reasoning process into three collaborative agents: (1) a solution planning agent that iteratively formulates sub-queries, (2) an Internet search agent that retrieves relevant documents via real-time web search, and (3) a structured webpage reading agent that extracts key evidence from raw web content. To rigorously evaluate deep reasoning abilities, we introduce $\textbf{ORION}$, a challenging benchmark focused on open-web reasoning over long-tail entities, covering both English and Chinese. Experimental results show that ManuSearch substantially outperforms prior open-source baselines and even surpasses leading closed-source systems. Our work paves the way for reproducible, extensible research in open deep search systems.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: Resources and Evaluation, Language Modeling
Contribution Types: Data resources, Data analysis
Languages Studied: English, Chinese
Keywords: Large Language Models, Multi-Agent, Deep Search
Submission Number: 3740
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