用假设驱动研究流程，让AI主动发现知识而非被动搜索。
Hypothesis-Driven Deep Research with Large Language Models: A Structured Methodology for Automated Knowledge Discovery
- 以假设为引擎组织研究，形成闭环迭代的探索机制。
- 事实密度提升22.4%，多源验证置信度达0.92，补全率提高14%。
- 适合需要系统性知识挖掘的研究者，尤其擅长跨领域深度探索。
当前AI科研系统采用‘直接搜索-摘要’范式，将假设视为科学发现的终点。我们提出假设驱动的深度研究（HDRI）方法——首个利用假设组织通用领域深度研究的框架，而非仅在特定领域验证命题。该方法将研究从被动信息检索转变为可验证、可迭代的主动知识发现。HDRI包含六项核心原则与八阶段流程，核心创新为基于差距的迭代研究机制：自动识别信息与逻辑缺口并触发定向补充调查。还引入可追溯推理链与置信度量化传播的推理框架、防止实体混淆的主题锁定机制，以及多维质量评估体系。该方法实现于INFOMINER系统。实验表明，事实密度提升22.4%，主题匹配准确率达90%，多源验证置信度达0.92，缺口补全带来14%完整度增益。五个案例研究验证其实用性，平均质量评分4.46/5.0。
原文摘要 · Abstract (English)
Current AI-powered research systems adopt a direct search-then-summarize paradigm that treats hypotheses as end products of scientific discovery. We argue this leaves a critical gap: hypotheses can serve a far more powerful role as organizational instruments that structure the research process itself. We propose the Hypothesis-Driven Deep Research (HDRI) methodology - the first framework using hypotheses to organize general-purpose deep research across arbitrary domains, rather than merely validating claims within specific domains. This transforms research from reactive information retrieval into proactive, verifiable, and iterative knowledge discovery. HDRI is formalized with six core principles and an eight-stage pipeline. A central innovation is the gap-driven iterative research mechanism - a closed-loop quality assurance system that automatically identifies informational and logical gaps, triggering targeted supplementary investigation. We further introduce a fact reasoning framework with traceable reasoning chains and quantified confidence propagation, a subject locking mechanism to prevent entity confusion, and a multi-dimensional quality assessment scheme. The methodology is realized in the INFOMINER system. Experiments demonstrate improvements of 22.4% in fact density, 90% subject matching accuracy, 0.92 multi-source verification confidence, and 14% completeness gain from gap-driven supplementation. Five case studies validate its practical applicability, achieving an average quality rating of 4.46/5.0.
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