DeepResearch reshapes the traditional research model through a standardized four-phase process: the problem disassembly phase employs mind-mapping analysis to break down the macro-problem into specific sub-tasks; the data collection phase intelligently assigns search engine queries and targeted crawling strategies; the analysis phase applies the inductive reasoning capabilities of the big language model; and the final summary phase generates a structured report with cited sources. This pipelined design reduces the completion time of typical research tasks by 60-80%.
Practical application cases show that in the topic of 'analyzing the application of blockchain in supply chain finance', the system automatically identifies four key dimensions (technology implementation, regulatory policy, case analysis and benefit assessment), extracts valid data from 37 credible sources, and ultimately generates a 15-page standard report in only 23 minutes. There are quality checkpoints at each step of the process to ensure that there is no deviation from the research direction. Researchers can also customize their own workflow by modifying the research plan template, and this flexibility is one of the tool's core competencies.
This answer comes from the articleDeepResearch: a fully open source AI assistant for automated deep researchThe
