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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.16075 |
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| _version_ | 1866915404103811072 |
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| author | Han, Rujun Chen, Yanfei CuiZhu, Zoey Miculicich, Lesly Sun, Guan Bi, Yuanjun Wen, Weiming Wan, Hui Wen, Chunfeng Maître, Solène Lee, George Tirumalashetty, Vishy Xue, Emily Zhang, Zizhao Haykal, Salem Gokturk, Burak Pfister, Tomas Lee, Chen-Yu |
| author_facet | Han, Rujun Chen, Yanfei CuiZhu, Zoey Miculicich, Lesly Sun, Guan Bi, Yuanjun Wen, Weiming Wan, Hui Wen, Chunfeng Maître, Solène Lee, George Tirumalashetty, Vishy Xue, Emily Zhang, Zizhao Haykal, Salem Gokturk, Burak Pfister, Tomas Lee, Chen-Yu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16075 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Researcher with Test-Time Diffusion Han, Rujun Chen, Yanfei CuiZhu, Zoey Miculicich, Lesly Sun, Guan Bi, Yuanjun Wen, Weiming Wan, Hui Wen, Chunfeng Maître, Solène Lee, George Tirumalashetty, Vishy Xue, Emily Zhang, Zizhao Haykal, Salem Gokturk, Burak Pfister, Tomas Lee, Chen-Yu Computation and Language 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. |
| title | Deep Researcher with Test-Time Diffusion |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.16075 |