Deep Research achieves a paradigm shift in research methodology with an iterative research mechanism that consists of four key steps: intelligent query generation using LLM to transform fuzzy requirements into precise search statements; dynamic result processing to extract key information points through semantic analysis; strategy optimization module to automatically adjust the research direction based on the intermediate results; and the final stage of knowledge integration to generate a structured research report. This adaptive research method improves efficiency significantly over the traditional one-time search, and studies have shown that the iterative approach can increase the depth of information acquisition by 3-5 times.
The system provides fine-grained research control parameters, including adjustable settings for depth level (depth) and breadth range (breadth). The technical implementation employs a recursive query optimization algorithm, where each iteration evaluates the information entropy value to determine the subsequent paths. The case study shows that for a complex topic such as "commercial applications of quantum computing", the information density of the report generated after 3 iterations is 72% higher than that of a single search.
This answer comes from the articleDeep Research: an AI-based deep research assistant that provides efficient research tools and report generation capabilitiesThe
