Deep Researcher with Test-Time Diffusion

• URL: http://arxiv.org/abs/2507.16075v1
• Date: Mon, 21 Jul 2025 21:23:21 GMT
• Title: Deep Researcher with Test-Time Diffusion
• Authors: Rujun Han, Yanfei Chen, Zoey CuiZhu, Lesly Miculicich, Guan Sun, Yuanjun Bi, Weiming Wen, Hui Wan, Chunfeng Wen, Solène Maître, George Lee, Vishy Tirumalashetty, Emily Xue, Zizhao Zhang, Salem Haykal, Burak Gokturk, Tomas Pfister, Chen-Yu Lee,
• Abstract summary: Test-Time Diffusion Deep Researcher conceptualizes research report generation as a diffusion process.<n>Draft-centric design makes the report writing process more timely and coherent.<n>We demonstrate that our TTD-DR achieves state-of-the-art results on a wide array of benchmarks.
• Score: 32.375428487905104
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Characterizing Deep Research: A Benchmark and Formal Definition [24.523394260858822]
  We propose a formal characterization of the deep research (DR) task and introduce a benchmark to evaluate the performance of DR systems.<n>We argue that the core defining feature of deep research is not the production of lengthy report-style outputs, but rather the high fan-out over concepts required during the search process.
  arXiv  Detail & Related papers  (2025-08-06T08:09:28Z)
• From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents [96.65646344634524]
  Large Language Models (LLMs), endowed with reasoning and agentic capabilities, are ushering in a new paradigm termed Agentic Deep Research.<n>We trace the evolution from static web search to interactive, agent-based systems that plan, explore, and learn.<n>We demonstrate that Agentic Deep Research not only significantly outperforms existing approaches, but is also poised to become the dominant paradigm for future information seeking.
  arXiv  Detail & Related papers  (2025-06-23T17:27:19Z)
• Deep Research Agents: A Systematic Examination And Roadmap [79.04813794804377]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)
• DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents [30.768405850755602]
  DeepResearch Bench is a benchmark consisting of 100 PhD-level research tasks.<n> evaluating Deep Research Agents is inherently complex and labor-intensive.<n>We propose two novel methodologies that achieve strong alignment with human judgment.
  arXiv  Detail & Related papers  (2025-06-13T13:17:32Z)
• Reinforcement Fine-Tuning for Reasoning towards Multi-Step Multi-Source Search in Large Language Models [7.719379471690927]
  Reasoning-Search (R-Search) is a single-LLM search framework that unifies multi-step planning, multi-source search execution, and answer synthesis.<n>R-Search structure the output into four explicitly defined components, including reasoning steps that guide the search process.
  arXiv  Detail & Related papers  (2025-06-10T02:09:57Z)
• EvolveSearch: An Iterative Self-Evolving Search Agent [98.18686493123785]
  Large language models (LLMs) have transformed agentic information seeking capabilities through the integration of tools such as search engines and web browsers.<n>We propose EvolveSearch, a novel iterative self-evolution framework that combines SFT and RL to enhance agentic web search capabilities without any external human-annotated reasoning data.
  arXiv  Detail & Related papers  (2025-05-28T15:50:48Z)
• Chain-of-Retrieval Augmented Generation [72.06205327186069]
  This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer.<n>Our proposed method, CoRAG, allows the model to dynamically reformulate the query based on the evolving state.
  arXiv  Detail & Related papers  (2025-01-24T09:12:52Z)
• State-Space Modeling in Long Sequence Processing: A Survey on Recurrence in the Transformer Era [59.279784235147254]
  This survey provides an in-depth summary of the latest approaches that are based on recurrent models for sequential data processing. The emerging picture suggests that there is room for thinking of novel routes, constituted by learning algorithms which depart from the standard Backpropagation Through Time.
  arXiv  Detail & Related papers  (2024-06-13T12:51:22Z)
• CorpusBrain: Pre-train a Generative Retrieval Model for Knowledge-Intensive Language Tasks [62.22920673080208]
  Single-step generative model can dramatically simplify the search process and be optimized in end-to-end manner. We name the pre-trained generative retrieval model as CorpusBrain as all information about the corpus is encoded in its parameters without the need of constructing additional index.
  arXiv  Detail & Related papers  (2022-08-16T10:22:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.