具备测试时扩散能力的深度研究者

摘要

依托大型语言模型（LLMs）驱动的深度研究智能体正迅速发展；然而，在利用通用测试时扩展算法生成复杂长篇研究报告时，其性能往往遭遇瓶颈。受人类研究中搜索、推理与修订循环迭代特性的启发，我们提出了测试时扩散深度研究者（TTD-DR）。这一创新框架将研究报告的生成概念化为一个扩散过程。TTD-DR以初步草稿启动该过程，该草稿作为可更新的框架，引导研究方向的演进基础。随后，通过一个“去噪”过程，草稿被迭代精炼，此过程动态地由检索机制所指导，每一步都融入外部信息。核心过程进一步通过应用于智能体工作流各环节的自进化算法得到增强，确保为扩散过程生成高质量上下文。这种以草稿为中心的设计，使报告撰写过程更加及时且连贯，同时减少了迭代搜索过程中的信息丢失。我们证明，TTD-DR在需要密集搜索与多跳推理的广泛基准测试中取得了最先进的成果，显著超越了现有的深度研究智能体。

English

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.

具备测试时扩散能力的深度研究者

Deep Researcher with Test-Time Diffusion

摘要

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