Deep Researcher with Test-Time Diffusion
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.
688501d57d7a19a208cdf03a
Rujun Han
688501d57d7a19a208cdf03b
Yanfei Chen
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Zoey CuiZhu
688501d57d7a19a208cdf03d
Lesly Miculicich
688501d57d7a19a208cdf03e
Guan Sun
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Yuanjun Bi
688501d57d7a19a208cdf040
Weiming Wen
688501d57d7a19a208cdf041
Hui Wan
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Chunfeng Wen
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Solène Maître
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George Lee
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Vishy Tirumalashetty
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Emily Xue
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Zizhao Zhang
688501d57d7a19a208cdf048
Salem Haykal
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Burak Gokturk
688501d57d7a19a208cdf04a
Tomas Pfister
688501d57d7a19a208cdf04b
Chen-Yu Lee
Deep Researcher with Test-Time Diffusion
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.
TTD-DR, a diffusion-based framework, generates high-quality research reports by iteratively refining a preliminary draft with external information and self-evolutionary algorithms, outperforming existing deep research agents.
Large Language Models (LLMs) Test-Time Diffusion Deep Researcher (TTD-DR) diffusion process preliminary draft denoising process retrieval mechanism self-evolutionary algorithm multi-hop reasoning