Self-evolution is critical in deep research (DR) agents as it empowers each component of the research workflow to undergo its optimization process. This approach ensures the exploration of diverse knowledge and mitigates information loss throughout the agentic trajectories, ultimately providing better context for report generation. By enhancing individual outputs, self-evolution significantly improves the overall quality and coherence of the research reports generated by DR agents, as emphasized by the authors[1].
In the Test-Time Diffusion Deep Researcher (TTD-DR), self-evolution works synergistically with the denoising mechanism, allowing for more comprehensive and timely integration of information during the report writing process. This iterative refinement is essential for achieving high-quality research outcomes that surpass existing DR agents[1].
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