Core Insights - The article emphasizes the increasing demand for efficient and precise information retrieval and decision support in the digital economy, highlighting the necessity of a "Deep Research System" that can extract key knowledge from vast heterogeneous data sources and perform multi-step reasoning [2][3]. Challenges in Existing Research Systems - Existing research systems face challenges in adapting to real-world enterprise environments, including static architectures, insufficient integration of private datasets, lack of automated evaluation and continuous optimization, and inadequate long-term memory and dynamic evolution mechanisms [5]. - Many systems rely on static prompts or fixed scripts, making them unable to learn and optimize from real-world feedback [5]. - Current research-oriented intelligent agents struggle to securely and efficiently integrate enterprise private data and lack dynamic optimization capabilities [5]. - There is a notable absence of automated evaluation and continuous optimization mechanisms in systems like Anthropic's Claude Research Workbench, hindering sustained improvement in deployment environments [5]. Dingtalk-DeepResearch Framework - Dingtalk-DeepResearch is introduced as a unified multi-agent intelligent framework designed for complex and evolving enterprise tasks, integrating deep research generation, heterogeneous table reasoning, and multi-modal report synthesis [3][10]. - The framework has achieved high scores in international deep research evaluations, ranking second globally and first domestically in the DeepResearch Bench [7]. - It has been successfully deployed in real enterprise scenarios such as manufacturing and supply chain, demonstrating industry-leading accuracy and robustness [10]. Framework Architecture - The Dingtalk-DeepResearch framework features a layered design, providing a comprehensive and flexible intelligent hub for enterprises [12]. - The framework includes specialized agents for deep research, table data processing, and data analysis, along with a core that integrates key functions such as context compression, reasoning, long-term memory, and human-machine collaboration [14]. - A unified data layer consolidates knowledge graphs, databases, and multi-modal datasets, facilitating diverse enterprise and industry data retrieval [14]. Adaptive Intelligence Mechanisms - The framework employs a multi-stage document reinforcement learning approach to enhance document generation capabilities, utilizing a reward model trained on approximately 800,000 labeled samples [17][18]. - An entropy-guided, memory-aware online learning mechanism allows the intelligent agent to adapt continuously to evolving tasks without frequent fine-tuning of the underlying LLM parameters [21]. - The system's table question-answering module effectively handles complex and heterogeneous table data, ensuring precise and interpretable reasoning [22][23]. Continuous Optimization and Evaluation - DingAutoEvaluator serves as a core driver for continuous evolution, transforming the development paradigm into a fully evaluation-driven approach [25]. - The platform continuously monitors cognitive uncertainty peaks in model outputs, prioritizing uncertain cases for expert annotation [25]. - A unified measurement framework evaluates various aspects of the framework's outputs, providing real-time signals for ongoing optimization [31]. Practical Applications and Case Studies - The article presents multiple real-world case studies demonstrating Dingtalk-DeepResearch's end-to-end capabilities in complex table data parsing, retrieval, reasoning, and multi-modal document generation [27]. - In one case, the system accurately processed a complex table containing inventory and logistics information, showcasing its robustness and practical utility [28]. - Another case involved the system answering production-related queries by effectively breaking down complex questions into manageable steps [30][32]. Future Outlook - Dingtalk-DeepResearch is set to be deployed in enterprise workflows and will soon be available as a service through Dingtalk, providing a robust solution for complex task management [44]. - The framework's adaptive capabilities, large-scale document reinforcement learning, and structured table reasoning position it as a significant advancement in enterprise-level adaptive intelligence [45].