WideResearcher: Knowledge-Graph-Guided Deep Research for Broader Exploration

Deep research tasks require an AI system to synthesize comprehensive, insightful long-form reports through extensive web search across tens to hundreds of sources. Despite steady recent progress, current agentic deep research systems still suffer from two major problems. First, the initial batch of search queries which strongly shapes the entire research trajectory can only depend on the parametric knowledge of the Large Language Models (LLMs), leading agents into deep "rabbit holes" at the expense of topic coverage breadth. Second, relations between different parts of the knowledge landscape are not easily discoverable from unstructured text, making it difficult for agents to recognize what they do not know, and consequently, which topics to explore next. To tackle these problems, we propose WideResearcher, a hierarchical multi-agent deep research system capable of performing high-quality research broadly and deeply. Our system conducts a preliminary exploration of an external knowledge base before any web search takes place, relieving the hard dependency on the LLM's parametric knowledge and ensuring broad coverage of the topic's knowledge landscape. A shared knowledge graph is then maintained throughout the research process, condensing unstructured web articles into structured entities and relations with provenance groundings, whose semantic and topological properties are exploited to guide subsequent research directions. We evaluate WideResearcher against a range of proprietary and open-source deep research systems on two established deep research benchmark, LiveResearchBench and DeepResearch Bench. Experimental results show that our system consistently produces higher-quality reports compared to existing approaches, showing the effectiveness our knowledge-graph-guided approach to deep research. The source code of WideResearcher is provided as supplementary material and will be publicly released upon acceptance.