The Test-Time Diffusion Deep Researcher (TTD-DR) stands apart from backbone deep research (DR) agents through its integrated framework that mimics human cognitive processes in research. Unlike traditional DR agents that often operate linearly or in parallel, the TTD-DR employs a draft-centric iterative process that facilitates continuous refinement through a dynamic feedback loop, enhancing coherence and reducing information loss during the research process[1].
TTD-DR features two core mechanisms: denoising with retrieval, which utilizes external information to revise reports, and self-evolution, which optimizes the performance of individual components in the research workflow. This holistic design results in superior performance on benchmarks that require intensive search and multi-hop reasoning tasks, significantly outperforming existing DR methods[1].
Get more accurate answers with Super Search, upload files, personalized discovery feed, save searches and contribute to the PandiPedia.
Let's look at alternatives:
- Modify the query.
- Start a new thread.
- Remove sources (if manually added).
- Request a manual search from our human research team.