Analytical information needs, such as trend analysis and causal impact assessment, are prevalent across various domains including law, finance, science, and much more. However, existing information retrieval paradigms, whether based on relevance-oriented document ranking or retrieval-augmented generation (RAG) with large language models (LLMs), often struggle to meet the end-to-end requirements of such tasks at the corpus scale. They either emphasize information finding rather than end-to-end problem solving, or simply treat everything as naive question answering, offering limited control over reasoning, evidence usage, and verifiability. As a result, they struggle to support analytical queries that have diverse utility concepts and high accountability requirements. In this paper, we propose analytical search as a distinct and emerging search paradigm designed to fulfill these analytical information needs. Analytical search reframes search as an evidence-governed, process-oriented analytical workflow that explicitly models analytical intent, retrieves evidence for fusion, and produces verifiable conclusions through structured, multi-step inference. We position analytical search in contrast to existing paradigms, and present a unified system framework that integrates query understanding, recall-oriented retrieval, reasoning-aware fusion, and adaptive verification. We also discuss potential research directions for the construction of analytical search engines. In this way, we highlight the conceptual significance and practical importance of analytical search and call on efforts toward the next generation of search engines that support analytical information needs.

翻译：分析性信息需求，如趋势分析和因果影响评估，普遍存在于法律、金融、科学等诸多领域。然而，现有的信息检索范式，无论是基于面向相关性的文档排序，还是结合大语言模型的检索增强生成，往往难以在语料库规模上满足此类任务的端到端需求。它们要么侧重于信息发现而非端到端的问题解决，要么简单地将一切视为朴素的问答任务，对推理过程、证据使用和可验证性的控制有限。因此，它们难以支持那些具有多样化效用概念和高问责要求的分析性查询。在本文中，我们提出分析式搜索作为一种独特且新兴的搜索范式，旨在满足这些分析性信息需求。分析式搜索将搜索重新定义为一种证据驱动、过程导向的分析工作流，它显式地建模分析意图，检索证据进行融合，并通过结构化的多步推理产生可验证的结论。我们将分析式搜索与现有范式进行对比，并提出一个统一的系统框架，该框架集成了查询理解、面向召回率的检索、推理感知的融合以及自适应验证。我们还讨论了构建分析式搜索引擎的潜在研究方向。通过这种方式，我们强调了分析式搜索的概念意义和实际重要性，并呼吁为支持分析性信息需求的下一代搜索引擎做出努力。