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

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.