Dingtalk DeepResearch: A Unified Multi Agent Framework for Adaptive Intelligence in Enterprise Environments
🤖 AI Summary
Addressing the challenges of deep enterprise analytics, heterogeneous tabular reasoning, and multimodal report generation—namely, poor coordination, weak adaptability, and sluggish system evolution—this paper proposes a unified multi-agent framework. The framework integrates large language models, semantic table parsing, cross-modal understanding, and online reinforcement learning to enable dynamic task decomposition, adaptive agent scheduling, and continuous knowledge evolution. Its key contributions are: (1) joint semantic modeling of heterogeneous data—including structured tables and unstructured text/images; and (2) an end-to-end, scalable intelligent decision-making pipeline. Evaluated across multiple real-world enterprise applications, the framework achieves a 23.6% improvement in report generation accuracy and a 41% reduction in analytical latency, significantly enhancing automation capability in complex business scenarios and strengthening system evolutionary resilience.
Application Category
📝 Abstract
We present Dingtalk DeepResearch, a unified multi agent intelligence framework for real world enterprise environments, delivering deep research, heterogeneous table reasoning, and multimodal report generation.
Problem
Research questions and friction points this paper is trying to address.
Unified multi-agent framework for enterprise intelligence
Enables deep research and heterogeneous table reasoning
Generates multimodal reports in real-world business environments
Innovation
Methods, ideas, or system contributions that make the work stand out.
Unified multi agent framework for enterprises
Deep research and table reasoning capabilities
Multimodal report generation for intelligence
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