Large language models have enabled the development of deep research (DR) agents, capable of various research tasks. Existing DR agents often lack the iterative process of human research, like planning and revision. Test-Time Diffusion Deep Researcher (TTD-DR) is introduced as a new agent that mimics human research processes. TTD-DR models report writing as a diffusion process, refining a draft through iterative cycles. It uses algorithms like component-wise self-evolution and report-level refinement. The agent starts with a research plan, iteratively generating search questions and synthesizing answers. Self-evolution improves each stage's performance by using feedback and revision loops. Report-level denoising uses a search tool to iteratively revise the draft with new information. TTD-DR achieves state-of-the-art results on long-form report writing and multi-hop reasoning benchmarks. Results show TTD-DR is more efficient and achieves better quality than competitors. The "draft-first" approach keeps the research process focused and coherent.