Search papers, labs, and topics across Lattice.
The paper introduces "analytical search" as a new search paradigm tailored for complex analytical information needs, addressing the limitations of relevance-based ranking and retrieval-augmented generation (RAG) in tasks requiring trend analysis, causal inference, and verifiable conclusions. It proposes a system framework that integrates query understanding, recall-oriented retrieval, reasoning-aware fusion, and adaptive verification to support structured, multi-step inference. The authors argue that analytical search offers improved control over reasoning, evidence usage, and verifiability, leading to more accountable and utility-driven results compared to existing search paradigms.
Current search paradigms fall short for analytical tasks, motivating a new "analytical search" framework that treats search as an evidence-driven, multi-step reasoning process.
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.
Tsinghua AI