Dingling AI achieves dual breakthroughs in global top-tier benchmarks and real-world applications, with its deep research system ranking second globally and first in China!

Recently, the "Dingtalk-DeepResearch" system, developed by the DingTalk team, has achieved a breakthrough result in an international authoritative evaluation, ranking second globally and first domestically on the DeepResearch Bench with a score of 48.49, surpassing mainstream systems such as OpenAI and Claude.

Deployment Across Multiple Scenarios Enables Intelligent Handling of Complex Tasks

The system has been successfully applied in complex scenarios such as manufacturing and supply chain management, maintaining industry-leading accuracy and robustness in handling heterogeneous tables, multi-stage reasoning, and multimodal generation tasks. It helps enterprises efficiently process multimodal data and achieve intelligent transformation.

This advancement marks the first time that DingTalk's deep research system has achieved dual breakthroughs—both on top-tier international benchmarks and in real-world production deployment—signaling that Chinese enterprise-level AI technology has entered the global leading tier.

Multi-Agent Architecture Supports Deep Collaborative Research

At the core of Dingtalk-DeepResearch is a multi-agent deep research framework designed for real enterprise environments, effectively integrating deep research generation, heterogeneous table parsing and reasoning, and multimodal report generation within a unified system.

This design resembles assembling team members with different specialized skills into one system: some excel at analyzing tabular data, others focus on report generation, while others coordinate tool usage. Through a three-layer architecture (task-oriented agent layer, core engine layer, data layer), the system supports parallel processing and multi-stage reasoning for complex tasks—for instance, automatically parsing factory production tables containing nested and merged cells, and transforming them into structured, insight-rich analytical reports.

Continuous Evolution Mechanism Enables Adaptive Learning

To adapt to the dynamic changes in enterprise environments, the framework employs an entropy-guided, memory-aware online learning mechanism that allows agents to evolve continuously—similar to how employees improve their skills through repeated practice, but without human intervention. This mechanism ensures the system can autonomously extract experience from historical interactions and gradually adapt to different business processes and user behavior patterns across organizations.

For example, when users repeatedly modify the format of AI-generated reports, the system learns and remembers their preferences regarding formatting, style, and key points, proactively aligning future outputs with these preferences. Within the DingTalk enterprise AI platform, such personalized optimizations can be retained as reusable capabilities and shared across teams or even entire companies, enabling knowledge reuse and enhanced organizational efficiency.

Closed-Loop Evaluation System Ensures Reliable Output Quality

To ensure the accuracy and reliability of generated content, Dingtalk-DeepResearch incorporates an internal evaluation system called DingAutoEvaluator. This system conducts multi-dimensional "quality inspections" on every generated report, covering aspects such as data accuracy, logical coherence, and proper tool usage. If issues are detected, the system automatically feeds relevant cases back into the training pipeline to refine the model, creating a continuous improvement loop from generation and evaluation to optimization.

Stable Application Across Industries Delivers Real-World Value

Currently, Dingtalk-DeepResearch has been stably deployed in multiple real-world business scenarios, delivering tangible value. In supply chain management, the system rapidly analyzes complex cross-departmental tabular data, providing intelligent recommendations for procurement strategies. In manufacturing, it automatically transforms raw equipment operation data into visualized analytical reports, supporting decision-making for predictive maintenance. All core capabilities have been validated through international benchmark testing, ensuring both technical reliability and leadership.

DingTalk CTO Zhu Hong stated, "By combining adaptive optimization with multimodal reasoning, Dingtalk-DeepResearch forms a flexible enterprise-grade AI framework designed to handle complex and evolving real-world business tasks. This technology is being rapidly integrated into products such as AI search, AI-powered spreadsheets, automated workflows, and Agent platforms, bringing cutting-edge AI closer to practical production needs and delivering truly valuable AI solutions for enterprises."

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