The main function of the Test-Time Diffusion Deep Researcher (TTD-DR) is to generate comprehensive research reports by mimicking the iterative nature of human research, which involves cycles of planning, drafting, searching for information, and revising. TTD-DR begins with a preliminary draft, which serves as a guiding framework that is iteratively refined through a 'denoising' process, dynamically informed by a retrieval mechanism that integrates external information at each step. This allows for timely and coherent integration of information while reducing information loss during the research process[1].
Additionally, TTD-DR employs a self-evolutionary algorithm to optimize each component of the research workflow, ensuring high-quality output throughout the report generation process[1].
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