# Dingtalk DeepResearch: A Unified Multi Agent Framework for Adaptive Intelligence in Enterprise Environments

arXiv: https://arxiv.org/abs/2510.24760 · HTML: https://mlsanity.com/paper/2510.24760/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 2 of 2)

- [Memento: Fine-tuning LLM Agents without Fine-tuning LLMs](https://mlsanity.com/paper/2508.16153/) (2025)
- [TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning](https://mlsanity.com/paper/2506.10380/) (2025)

## Built on by (most cited first, 5 of 5)

- [How to Train a Real-World Silicon Concierge? Internalizing Complex Business Workflow to Only OneModel](https://mlsanity.com/paper/2608.20350/) (2026)
- [Hypergraph Enterprise Agentic Reasoner over Heterogeneous Business Systems](https://mlsanity.com/paper/2605.14259/) (2026)
- [ARC: Fair Relative Advantage Comparison in Open-Ended Real-World Interaction](https://mlsanity.com/paper/2608.13622/) (2026)
- [Grounded Skill Synthesis from Code at Scale for Agentic Intelligence](https://mlsanity.com/paper/2609.05571/) (2026)
- [Faithful Chart Generation for Multimodal Deep Research: Frame-Evidence Co-Adaptation](https://mlsanity.com/paper/2610.00374/) (2026)

## Abstract (arXiv)

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.