DeepDiver-V2来了，华为最新开源原生多智能体系统，“团战”深度研究效果惊人

Core Insights - The article discusses Huawei's latest release, DeepDiver-V2, a native multi-agent system designed for deep research, which utilizes a "teamwork" approach for task execution and information sharing [1][2]. Group 1: System Architecture and Functionality - DeepDiver-V2 employs a multi-agent system (MAS) architecture, featuring a Planner for task decomposition and multiple Executors for parallel processing of sub-tasks, enhancing efficiency [1][7]. - The system is capable of generating high-quality deep research reports, achieving an average report length of 24.6K tokens, significantly surpassing competitors like OpenAI's DeepResearch [4][2]. - The architecture allows for specialized roles among Executors, including Information Seekers for data collection and Writers for long-text generation, improving overall output quality [12][21]. Group 2: Performance Metrics - In benchmark tests, DeepDiver-V2-38B scored 34.6 in BrowseComp-zh, outperforming WebSailor-72B and other models, while DeepDiver-V2-7B also exceeded similar models [5][4]. - The system's performance is sensitive to the capabilities of Executors, indicating that their effectiveness is crucial for overall system performance [19][21]. Group 3: Training and Optimization - The training process involves multi-stage optimization, including supervised fine-tuning and rejection sampling techniques, which enhance the model's collaborative capabilities [15][16]. - The training data has been expanded to include more challenging and long-form writing tasks, contributing to the improved performance of DeepDiver-V2 [16][27]. Group 4: Future Implications - The transition from a single model to a multi-agent system represents a new paradigm in AI search, with potential applications in enterprise research, scientific literature reviews, and professional data analysis [27][28].