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New open-weight agents tackle deep research tasks with synthetic data and novel architectures

By PulseAugur Editorial · [4 sources] ·

Two new research papers introduce advanced agent systems designed for deep research tasks. The first, QUEST, offers a family of open-weight models (2B to 35B parameters) trained on synthetic data, demonstrating strong performance in fact-seeking, citation grounding, and report synthesis, rivaling proprietary agents. The second, Argus, presents a cooperative Searcher-Navigator system built on a 35B MoE backbone, which excels at assembling evidence from complementary sources, achieving state-of-the-art results on benchmarks like BrowseComp while maintaining a manageable context window. AI

IMPACT These advancements in open-weight deep research agents and scalable evidence assembly could accelerate knowledge synthesis and democratize access to advanced AI research capabilities.

RANK_REASON Two distinct research papers introducing new agent architectures and training methodologies for deep research tasks.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New open-weight agents tackle deep research tasks with synthetic data and novel architectures

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Two distinct research papers introducing new agent architectures and training methodologies for deep research tasks.
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paper, model release
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COVERAGE [4]

  1. arXiv cs.CL TIER_1 English(EN) · Jian Xie, Tianhe Lin, Zilu Wang, Yuting Ning, Yuekun Yao, Tianci Xue, Zhehao Zhang, Zhongyang Li, Kai Zhang, Yufan Wu, Shijie Chen, Boyu Gou, Mingzhe Han, Yifei Wang, Vint Lee, Xinpeng Wei, Xiangjun Wang, Yu Su, Huan Sun ·

    QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks

    arXiv:2605.24218v1 Announce Type: new Abstract: Deep research agents extend the role of search engines from retrieving keyword-matched pages to synthesizing knowledge, fundamentally changing how humans interact with information. However, frontier systems remain proprietary, while…

  2. arXiv cs.AI TIER_1 English(EN) · Zhen Zhang, Liangcai Su, Zhuo Chen, Xiang Lin, Haotian Xu, Simon Shaolei Du, Kaiyu Yang, Bo An, Lidong Bing, Xinyu Wang ·

    Argus: Evidence Assembly for Scalable Deep Research Agents

    arXiv:2605.16217v3 Announce Type: replace-cross Abstract: Deep research agents have achieved remarkable progress on complex information seeking tasks. Even long ReAct style rollouts explore only a single trajectory, while recent state of the art systems scale inference time compu…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks

    QUEST is an open-family of deep research agents trained with synthesized data and reinforcement learning to perform well across diverse long-horizon search tasks.

  4. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Xinyu Wang ·

    Argus: Evidence Assembly for Scalable Deep Research Agents

    Deep research agents have achieved remarkable progress on complex information seeking tasks. Even long ReAct style rollouts explore only a single trajectory, while recent state of the art systems scale inference time compute via parallel search and aggregation. Yet deep research …

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