Deep Researcher with Test-Time Diffusion
Authors: name: Rujun Han; name: Yanfei Chen; name: Zoey CuiZhu; name: Lesly Miculicich; name: Guan Sun; name: Yuanjun Bi; name: Weiming Wen; name: Hui Wan; name: Chunfeng Wen; name: Solène Maître; name: George Lee; name: Vishy Tirumalashetty; name: Emily Xue; name: Zizhao Zhang; name: Salem Haykal; name: Burak Gokturk; name: Tomas Pfister; name: Chen-Yu Lee
Published: 2025-07-21 21:23:21

Abstract: Deep research agents, powered by Large Language Models (LLMs), are rapidly
advancing; yet, their performance often plateaus when generating complex,
long-form research reports using generic test-time scaling algorithms. Drawing
inspiration from the iterative nature of human research, which involves cycles
of searching, reasoning, and revision, we propose the Test-Time Diffusion Deep
Researcher (TTD-DR). This novel framework conceptualizes research report
generation as a diffusion process. TTD-DR initiates this process with a
preliminary draft, an updatable skeleton that serves as an evolving foundation
to guide the research direction. The draft is then iteratively refined through
a "denoising" process, which is dynamically informed by a retrieval mechanism
that incorporates external information at each step. The core process is
further enhanced by a self-evolutionary algorithm applied to each component of
the agentic workflow, ensuring the generation of high-quality context for the
diffusion process. This draft-centric design makes the report writing process
more timely and coherent while reducing information loss during the iterative
search process. We demonstrate that our TTD-DR achieves state-of-the-art
results on a wide array of benchmarks that require intensive search and
multi-hop reasoning, significantly outperforming existing deep research agents.