[2603.28376] Marco DeepResearch: Unlocking Efficient Deep Research Agents via Verification-Centric Design

Computer Science > Computation and Language arXiv:2603.28376 (cs) [Submitted on 30 Mar 2026] Title:Marco DeepResearch: Unlocking Efficient Deep Research Agents via Verification-Centric Design Authors:Bin Zhu, Qianghuai Jia, Tian Lan, Junyang Ren, Feng Gu, Feihu Jiang, Longyue Wang, Zhao Xu, Weihua Luo View a PDF of the paper titled Marco DeepResearch: Unlocking Efficient Deep Research Agents via Verification-Centric Design, by Bin Zhu and 8 other authors View PDF HTML (experimental) Abstract:Deep research agents autonomously conduct open-ended investigations, integrating complex information retrieval with multi-step reasoning across diverse sources to solve real-world problems. To sustain this capability on long-horizon tasks, reliable verification is critical during both training and inference. A major bottleneck in existing paradigms stems from the lack of explicit verification mechanisms in QA data synthesis, trajectory construction, and test-time scaling. Errors introduced at each stage propagate downstream and degrade the overall agent performance. To address this, we present Marco DeepResearch, a deep research agent optimized with a verification-centric framework design at three levels: \textbf{(1)~QA Data Synthesis:} We introduce verification mechanisms to graph-based and agent-based QA synthesis to control question difficulty while ensuring answers are unique and correct; \textbf{(2)~Trajectory Construction:} We design a verification-driven trajectory synthesis met...