Yunque DeepResearch Technical Report
- URL: http://arxiv.org/abs/2601.19578v1
- Date: Tue, 27 Jan 2026 13:10:00 GMT
- Title: Yunque DeepResearch Technical Report
- Authors: Yuxuan Cai, Xinyi Lai, Peng Yuan, Weiting Liu, Huajian Li, Mingda Li, Xinghua Wang, Shengxie Zheng, Yanchao Hao, Yuyang Yin, Zheng Wei,
- Abstract summary: Yunque DeepResearch is a hierarchical, modular, and robust framework for deep research.<n>It achieves state-of-the-art performance across a range of agentic deep research benchmarks.<n>We open-source the framework, reproducible implementations, and application cases to empower the community.
- Score: 12.184074646161223
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep research has emerged as a transformative capability for autonomous agents, empowering Large Language Models to navigate complex, open-ended tasks. However, realizing its full potential is hindered by critical limitations, including escalating contextual noise in long-horizon tasks, fragility leading to cascading errors, and a lack of modular extensibility. To address these challenges, we introduce Yunque DeepResearch, a hierarchical, modular, and robust framework. The architecture is characterized by three key components: (1) a centralized Multi-Agent Orchestration System that routes subtasks to an Atomic Capability Pool of tools and specialized sub-agents; (2) a Dynamic Context Management mechanism that structures completed sub-goals into semantic summaries to mitigate information overload; and (3) a proactive Supervisor Module that ensures resilience through active anomaly detection and context pruning. Yunque DeepResearch achieves state-of-the-art performance across a range of agentic deep research benchmarks, including GAIA, BrowseComp, BrowseComp-ZH, and Humanity's Last Exam. We open-source the framework, reproducible implementations, and application cases to empower the community.
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