Update README.md

README.md

@@ -1,329 +1,331 @@
----
-license: apache-2.0
----
-# AgentCPM-Report: Gemini-2.5-pro-DeepResearch Level Local DeepResearch
-
-<p align="center">
-<a href='https://huggingface.co/openbmb/AgentCPM-Report'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-AgentCPM--Report-yellow'>
-<a href='https://huggingface.co/openbmb/AgentCPM-Report-GGUF'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-AgentCPM--Report--GGUF-yellow'>
-<a href='https://github.com/OpenBMB/AgentCPM'><img src='https://img.shields.io/badge/GitHub-AgentCPM-blue?logo=github'>
-<a href='https://github.com/OpenBMB/UltraRAG'><img src='https://img.shields.io/badge/GitHub-UltraRAG-blue?logo=github'>
-</p>
-
-## Links
-- [AgentCPM-Report](https://huggingface.co/openbmb/AgentCPM-Report) The Gemini-2.5-pro-DeepResearch Level Local DeepResearch Model
-- [AgentCPM-Report-GGUF](https://huggingface.co/openbmb/AgentCPM-Report-GGUF) The GGUF version
-- [AgentCPM](https://github.com/OpenBMB/AgentCPM) Our code for AgentCPM Series
-- [UltraRAG](https://github.com/OpenBMB/UltraRAG) The low code RAG Framework
-
-
-## News
-- [2026-01-20] 🚀🚀🚀 We open-sourced AgentCPM-Report built on MiniCPM4.1-8B, capable of matching top closed-source commercial systems like Gemini-2.5-pro-DeepResearch in report generation.
-
-## Overview
-AgentCPM-Report is an open-source large language model agent jointly developed by [THUNLP](https://nlp.csai.tsinghua.edu.cn), Renmin University of China [RUCBM](https://github.com/RUCBM), and [ModelBest](https://modelbest.cn/en). It is based on the [MiniCPM4.1](https://github.com/OpenBMB/MiniCPM4.1) 8B-parameter base model. It accepts user instructions as input and autonomously generates long-form reports. Key highlights:
-
-- **Strong advantages in insight and comprehensiveness**: The first 8B edge-side model to surpass closed-source DeepResearch systems on deep research report generation tasks, redefining the performance ceiling for small-scale agent systems—especially achieving SOTA results on the Insight metric.
-- **Lightweight and local deployment**: Supports agile local deployment. With frameworks like UltraRAG, it enables large-scale knowledge base construction and can generate reports that are even more professional and in-depth than large models. Lightweight models plus local knowledge bases make it feasible to deploy a deep-research report writing system on a personal computer, laying the foundation for report writing based on personal privacy data or private-domain data.
-
-## Demo Cases
-`YouTube link or Bilibili link for the video`
-
-## Quick Start
-### Docker Deployment
-We provide a minimal one-click `docker-compose` deployment integrated with UltraRAG, including the RAG framework UltraRAG2.0, the model inference framework vllm, and the vector database milvus. If you want CPU inference, we also provide a llama.cpp-based version for gguf models—just switch `docker-compose.yml` to `docker-compose.cpu.yml`.
-
-``` bash
-git clone git@github.com:OpenBMB/UltraRAG.git
-cd UltraRAG
-git checkout agentcpm-report-demo
-cd agentcpm-report-demo
-cp env.example .env
-docker-compose -f docker-compose.yml up -d --build
-docker-compose -f docker-compose.yml logs -f ultrarag-ui
-``` 
-The first startup pulls images, downloads the model, and configures the environment, which takes about 30 minutes.
-Then open `http://localhost:5050`. If you can see the UI, your deployment is successful.
-Follow the UI instructions to upload local files, chunk them, and build indexes; then in the Chat section, select AgentCPM-Report in the pipeline to start your workflow.
-
-(Optional) You can import [Wiki2024](https://modelscope.cn/datasets/UltraRAG/UltraRAG_Benchmark/tree/master/corpus/wiki24) as the writing database.
-
-You can read more tutorials about AgentCPM-Report in the [documentation](https://ultrarag.openbmb.cn/pages/cn/pipeline/agentcpm-report).
-
-
-## Evaluation
-<table align="center">
-  <thead>
-    <tr>
-      <th align="center">DeepResearch Bench</th>
-      <th align="center">Overall</th>
-      <th align="center">Comprehensiveness</th>
-      <th align="center">Insight</th>
-      <th align="center">Instruction Following</th>
-      <th align="center">Readability</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <td align="center">Doubao-research</td>
-      <td align="center">44.34</td>
-      <td align="center">44.84</td>
-      <td align="center">40.56</td>
-      <td align="center">47.95</td>
-      <td align="center">44.69</td>
-    </tr>
-    <tr>
-      <td align="center">Claude-research</td>
-      <td align="center">45</td>
-      <td align="center">45.34</td>
-      <td align="center">42.79</td>
-      <td align="center">47.58</td>
-      <td align="center">44.66</td>
-    </tr>
-    <tr>
-      <td align="center">OpenAI-deepresearch</td>
-      <td align="center">46.45</td>
-      <td align="center">46.46</td>
-      <td align="center">43.73</td>
-      <td align="center">49.39</td>
-      <td align="center">47.22</td>
-    </tr>
-    <tr>
-      <td align="center">Gemini-2.5-Pro-deepresearch</td>
-      <td align="center">49.71</td>
-      <td align="center">49.51</td>
-      <td align="center">49.45</td>
-      <td align="center">50.12</td>
-      <td align="center">50</td>
-    </tr>
-    <tr>
-      <td align="center">WebWeaver(Qwen3-30B-A3B)</td>
-      <td align="center">46.77</td>
-      <td align="center">45.15</td>
-      <td align="center">45.78</td>
-      <td align="center">49.21</td>
-      <td align="center">47.34</td>
-    </tr>
-    <tr>
-      <td align="center">WebWeaver(Claude-Sonnet-4)</td>
-      <td align="center">50.58</td>
-      <td align="center">51.45</td>
-      <td align="center">50.02</td>
-      <td align="center">50.81</td>
-      <td align="center">49.79</td>
-    </tr>
-    <tr>
-      <td align="center">Enterprise-DR(Gemini-2.5-Pro)</td>
-      <td align="center">49.86</td>
-      <td align="center">49.01</td>
-      <td align="center">50.28</td>
-      <td align="center">50.03</td>
-      <td align="center">49.98</td>
-    </tr>
-    <tr>
-      <td align="center">RhinoInsigh(Gemini-2.5-Pro)</td>
-      <td align="center">50.92</td>
-      <td align="center">50.51</td>
-      <td align="center">51.45</td>
-      <td align="center">51.72</td>
-      <td align="center">50</td>
-    </tr>
-    <tr>
-      <td align="center">AgentCPM-Report</td>
-      <td align="center">50.11</td>
-      <td align="center">50.54</td>
-      <td align="center">52.64</td>
-      <td align="center">48.87</td>
-      <td align="center">44.17</td>
-    </tr>
-  </tbody>
-</table>
-
-<table align="center">
-  <thead>
-    <tr>
-      <th align="center">DeepResearch Gym</th>
-      <th align="center">Avg.</th>
-      <th align="center">Clarity</th>
-      <th align="center">Depth</th>
-      <th align="center">Balance</th>
-      <th align="center">Breadth</th>
-      <th align="center">Support</th>
-      <th align="center">Insightfulness</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <td align="center">Doubao-research</td>
-      <td align="center">84.46</td>
-      <td align="center">68.85</td>
-      <td align="center">93.12</td>
-      <td align="center">83.96</td>
-      <td align="center">93.33</td>
-      <td align="center">84.38</td>
-      <td align="center">83.12</td>
-    </tr>
-    <tr>
-      <td align="center">Claude-research</td>
-      <td align="center">80.25</td>
-      <td align="center">86.67</td>
-      <td align="center">96.88</td>
-      <td align="center">84.41</td>
-      <td align="center">96.56</td>
-      <td align="center">26.77</td>
-      <td align="center">90.22</td>
-    </tr>
-    <tr>
-      <td align="center">OpenAI-deepresearch</td>
-      <td align="center">91.27</td>
-      <td align="center">84.90</td>
-      <td align="center">98.10</td>
-      <td align="center">89.80</td>
-      <td align="center">97.40</td>
-      <td align="center">88.40</td>
-      <td align="center">89.00</td>
-    </tr>
-    <tr>
-      <td align="center">Gemini-2.5-pro-deepresearch</td>
-      <td align="center">96.02</td>
-      <td align="center">90.71</td>
-      <td align="center">99.90</td>
-      <td align="center">93.37</td>
-      <td align="center">99.69</td>
-      <td align="center">95.00</td>
-      <td align="center">97.45</td>
-    </tr>
-    <tr>
-      <td align="center">WebWeaver (Qwen3-30b-a3b)</td>
-      <td align="center">77.27</td>
-      <td align="center">71.88</td>
-      <td align="center">85.51</td>
-      <td align="center">75.80</td>
-      <td align="center">84.78</td>
-      <td align="center">63.77</td>
-      <td align="center">81.88</td>
-    </tr>
-    <tr>
-      <td align="center">WebWeaver (Claude-sonnet-4)</td>
-      <td align="center">96.77</td>
-      <td align="center">90.50</td>
-      <td align="center">99.87</td>
-      <td align="center">94.30</td>
-      <td align="center">100.00</td>
-      <td align="center">98.73</td>
-      <td align="center">97.22</td>
-    </tr>
-    <tr>
-      <td align="center">AgentCPM-Report</td>
-      <td align="center">98.48</td>
-      <td align="center">95.1</td>
-      <td align="center">100.0</td>
-      <td align="center">98.5</td>
-      <td align="center">100.0</td>
-      <td align="center">97.3</td>
-      <td align="center">100.0</td>
-    </tr>
-  </tbody>
-</table>
-
-<table align="center">
-  <thead>
-    <tr>
-      <th align="center">DeepConsult</th>
-      <th align="center">Avg.</th>
-      <th align="center">Win</th>
-      <th align="center">Tie</th>
-      <th align="center">Lose</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <td align="center">Doubao-research</td>
-      <td align="center">5.42</td>
-      <td align="center">29.95</td>
-      <td align="center">40.35</td>
-      <td align="center">29.7</td>
-    </tr>
-    <tr>
-      <td align="center">Claude-research</td>
-      <td align="center">4.6</td>
-      <td align="center">25</td>
-      <td align="center">38.89</td>
-      <td align="center">36.11</td>
-    </tr>
-    <tr>
-      <td align="center">OpenAI-deepresearch</td>
-      <td align="center">5</td>
-      <td align="center">0</td>
-      <td align="center">100</td>
-      <td align="center">0</td>
-    </tr>
-    <tr>
-      <td align="center">Gemini-2.5-Pro-deepresearch</td>
-      <td align="center">6.7</td>
-      <td align="center">61.27</td>
-      <td align="center">31.13</td>
-      <td align="center">7.6</td>
-    </tr>
-    <tr>
-      <td align="center">WebWeaver(Qwen3-30B-A3B)</td>
-      <td align="center">4.57</td>
-      <td align="center">28.65</td>
-      <td align="center">34.9</td>
-      <td align="center">36.46</td>
-    </tr>
-    <tr>
-      <td align="center">WebWeaver(Claude-Sonnet-4)</td>
-      <td align="center">6.96</td>
-      <td align="center">66.86</td>
-      <td align="center">10.47</td>
-      <td align="center">22.67</td>
-    </tr>
-    <tr>
-      <td align="center">Enterprise-DR(Gemini-2.5-Pro)</td>
-      <td align="center">6.82</td>
-      <td align="center">71.57</td>
-      <td align="center">19.12</td>
-      <td align="center">9.31</td>
-    </tr>
-    <tr>
-      <td align="center">RhinoInsigh(Gemini-2.5-Pro)</td>
-      <td align="center">6.82</td>
-      <td align="center">68.51</td>
-      <td align="center">11.02</td>
-      <td align="center">20.47</td>
-    </tr>
-    <tr>
-      <td align="center">AgentCPM-Report</td>
-      <td align="center">6.6</td>
-      <td align="center">57.6</td>
-      <td align="center">13.73</td>
-      <td align="center">28.68</td>
-    </tr>
-  </tbody>
-</table>
-
-Our evaluation datasets include DeepResearch Bench, DeepConsult, and DeepResearch Gym. The writing-time knowledge base includes about 2.7 million [Arxiv papers](https://www.kaggle.com/api/v1/datasets/download/Cornell-University/arxiv) and about 200,000 internal webpage summaries.
-
-## Acknowledgements
-This project would not be possible without the support and contributions of the open-source community. During development, we referred to and used multiple excellent open-source frameworks, models, and data resources, including [verl](https://github.com/volcengine/verl), [UltraRAG](https://github.com/OpenBMB/UltraRAG), [MiniCPM4.1](https://github.com/OpenBMB/MiniCPM4.1), and [SurveyGo](https://surveygo.modelbest.cn/).
-
-## Contributions
-Project leads: Yishan Li, Wentong Chen
-
-Contributors: Yishan Li, Wentong Chen, Yukun Yan, Mingwei Li, Sen Mei, Xiaorong Wang, Kunpeng Liu, Xin Cong, Shuo Wang, Zhong Zhang, Yaxi Lu, Zhenghao Liu, Yankai Lin, Zhiyuan Liu, Maosong Sun
-
-Advisors: Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun
-
-## Citation
-
-If **AgentCPM-Report** is helpful for your research, please cite it as follows:
-
-```bibtex
-@software{AgentCPMReport2026,
-  title  = {AgentCPM-Report: Gemini-2.5-pro-DeepResearch Level Local DeepResearch},
-  author = {Yishan Li, Wentong Chen, Yukun Yan, Mingwei Li, Sen Mei, Xiaorong Wang, Kunpeng Liu, Xin Cong, Shuo Wang, Zhong Zhang, Yaxi Lu, Zhenghao Liu, Yankai Lin, Zhiyuan Liu, Maosong Sun},
-  year   = {2026},
-  url    = {https://github.com/OpenBMB/AgentCPM}
-}
-```
+---
+license: apache-2.0
+---
+# AgentCPM-Report: Gemini-2.5-pro-DeepResearch Level Local DeepResearch
+
+<p align="center">
+<a href='https://huggingface.co/openbmb/AgentCPM-Report'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-AgentCPM--Report-yellow'>
+<a href='https://huggingface.co/openbmb/AgentCPM-Report-GGUF'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-AgentCPM--Report--GGUF-yellow'>
+<a href='https://github.com/OpenBMB/AgentCPM'><img src='https://img.shields.io/badge/GitHub-AgentCPM-blue?logo=github'>
+<a href='https://github.com/OpenBMB/UltraRAG'><img src='https://img.shields.io/badge/GitHub-UltraRAG-blue?logo=github'>
+</p>
+
+## Links
+- [AgentCPM-Report](https://huggingface.co/openbmb/AgentCPM-Report) The Gemini-2.5-pro-DeepResearch Level Local DeepResearch Model
+- [AgentCPM-Report-GGUF](https://huggingface.co/openbmb/AgentCPM-Report-GGUF) The GGUF version of AgentCPM-Report
+- [AgentCPM-Explore](https://huggingface.co/openbmb/AgentCPM-Explore) The first open-source agent model with 4B parameters to appear on 8 widely used long-horizon agent benchmarks.
+- [AgentCPM-Explore-GGUF](https://huggingface.co/openbmb/AgentCPM-Explore-GGUF) The GGUF version of AgentCPM-Explore
+- [AgentCPM](https://github.com/OpenBMB/AgentCPM) Our code for AgentCPM Series
+- [UltraRAG](https://github.com/OpenBMB/UltraRAG) The low code RAG Framework
+
+
+## News
+- [2026-01-20] 🚀🚀🚀 We open-sourced AgentCPM-Report built on MiniCPM4.1-8B, capable of matching top closed-source commercial systems like Gemini-2.5-pro-DeepResearch in report generation.
+
+## Overview
+AgentCPM-Report is an open-source large language model agent jointly developed by [THUNLP](https://nlp.csai.tsinghua.edu.cn), Renmin University of China [RUCBM](https://github.com/RUCBM), and [ModelBest](https://modelbest.cn/en). It is based on the [MiniCPM4.1](https://github.com/OpenBMB/MiniCPM4.1) 8B-parameter base model. It accepts user instructions as input and autonomously generates long-form reports. Key highlights:
+
+- **Strong advantages in insight and comprehensiveness**: The first 8B edge-side model to surpass closed-source DeepResearch systems on deep research report generation tasks, redefining the performance ceiling for small-scale agent systems—especially achieving SOTA results on the Insight metric.
+- **Lightweight and local deployment**: Supports agile local deployment. With frameworks like UltraRAG, it enables large-scale knowledge base construction and can generate reports that are even more professional and in-depth than large models. Lightweight models plus local knowledge bases make it feasible to deploy a deep-research report writing system on a personal computer, laying the foundation for report writing based on personal privacy data or private-domain data.
+
+## Demo Cases
+`YouTube link or Bilibili link for the video`
+
+## Quick Start
+### Docker Deployment
+We provide a minimal one-click `docker-compose` deployment integrated with UltraRAG, including the RAG framework UltraRAG2.0, the model inference framework vllm, and the vector database milvus. If you want CPU inference, we also provide a llama.cpp-based version for gguf models—just switch `docker-compose.yml` to `docker-compose.cpu.yml`.
+
+``` bash
+git clone git@github.com:OpenBMB/UltraRAG.git
+cd UltraRAG
+git checkout agentcpm-report-demo
+cd agentcpm-report-demo
+cp env.example .env
+docker-compose -f docker-compose.yml up -d --build
+docker-compose -f docker-compose.yml logs -f ultrarag-ui
+``` 
+The first startup pulls images, downloads the model, and configures the environment, which takes about 30 minutes.
+Then open `http://localhost:5050`. If you can see the UI, your deployment is successful.
+Follow the UI instructions to upload local files, chunk them, and build indexes; then in the Chat section, select AgentCPM-Report in the pipeline to start your workflow.
+
+(Optional) You can import [Wiki2024](https://modelscope.cn/datasets/UltraRAG/UltraRAG_Benchmark/tree/master/corpus/wiki24) as the writing database.
+
+You can read more tutorials about AgentCPM-Report in the [documentation](https://ultrarag.openbmb.cn/pages/cn/pipeline/agentcpm-report).
+
+
+## Evaluation
+<table align="center">
+  <thead>
+    <tr>
+      <th align="center">DeepResearch Bench</th>
+      <th align="center">Overall</th>
+      <th align="center">Comprehensiveness</th>
+      <th align="center">Insight</th>
+      <th align="center">Instruction Following</th>
+      <th align="center">Readability</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td align="center">Doubao-research</td>
+      <td align="center">44.34</td>
+      <td align="center">44.84</td>
+      <td align="center">40.56</td>
+      <td align="center">47.95</td>
+      <td align="center">44.69</td>
+    </tr>
+    <tr>
+      <td align="center">Claude-research</td>
+      <td align="center">45</td>
+      <td align="center">45.34</td>
+      <td align="center">42.79</td>
+      <td align="center">47.58</td>
+      <td align="center">44.66</td>
+    </tr>
+    <tr>
+      <td align="center">OpenAI-deepresearch</td>
+      <td align="center">46.45</td>
+      <td align="center">46.46</td>
+      <td align="center">43.73</td>
+      <td align="center">49.39</td>
+      <td align="center">47.22</td>
+    </tr>
+    <tr>
+      <td align="center">Gemini-2.5-Pro-deepresearch</td>
+      <td align="center">49.71</td>
+      <td align="center">49.51</td>
+      <td align="center">49.45</td>
+      <td align="center">50.12</td>
+      <td align="center">50</td>
+    </tr>
+    <tr>
+      <td align="center">WebWeaver(Qwen3-30B-A3B)</td>
+      <td align="center">46.77</td>
+      <td align="center">45.15</td>
+      <td align="center">45.78</td>
+      <td align="center">49.21</td>
+      <td align="center">47.34</td>
+    </tr>
+    <tr>
+      <td align="center">WebWeaver(Claude-Sonnet-4)</td>
+      <td align="center">50.58</td>
+      <td align="center">51.45</td>
+      <td align="center">50.02</td>
+      <td align="center">50.81</td>
+      <td align="center">49.79</td>
+    </tr>
+    <tr>
+      <td align="center">Enterprise-DR(Gemini-2.5-Pro)</td>
+      <td align="center">49.86</td>
+      <td align="center">49.01</td>
+      <td align="center">50.28</td>
+      <td align="center">50.03</td>
+      <td align="center">49.98</td>
+    </tr>
+    <tr>
+      <td align="center">RhinoInsigh(Gemini-2.5-Pro)</td>
+      <td align="center">50.92</td>
+      <td align="center">50.51</td>
+      <td align="center">51.45</td>
+      <td align="center">51.72</td>
+      <td align="center">50</td>
+    </tr>
+    <tr>
+      <td align="center">AgentCPM-Report</td>
+      <td align="center">50.11</td>
+      <td align="center">50.54</td>
+      <td align="center">52.64</td>
+      <td align="center">48.87</td>
+      <td align="center">44.17</td>
+    </tr>
+  </tbody>
+</table>
+
+<table align="center">
+  <thead>
+    <tr>
+      <th align="center">DeepResearch Gym</th>
+      <th align="center">Avg.</th>
+      <th align="center">Clarity</th>
+      <th align="center">Depth</th>
+      <th align="center">Balance</th>
+      <th align="center">Breadth</th>
+      <th align="center">Support</th>
+      <th align="center">Insightfulness</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td align="center">Doubao-research</td>
+      <td align="center">84.46</td>
+      <td align="center">68.85</td>
+      <td align="center">93.12</td>
+      <td align="center">83.96</td>
+      <td align="center">93.33</td>
+      <td align="center">84.38</td>
+      <td align="center">83.12</td>
+    </tr>
+    <tr>
+      <td align="center">Claude-research</td>
+      <td align="center">80.25</td>
+      <td align="center">86.67</td>
+      <td align="center">96.88</td>
+      <td align="center">84.41</td>
+      <td align="center">96.56</td>
+      <td align="center">26.77</td>
+      <td align="center">90.22</td>
+    </tr>
+    <tr>
+      <td align="center">OpenAI-deepresearch</td>
+      <td align="center">91.27</td>
+      <td align="center">84.90</td>
+      <td align="center">98.10</td>
+      <td align="center">89.80</td>
+      <td align="center">97.40</td>
+      <td align="center">88.40</td>
+      <td align="center">89.00</td>
+    </tr>
+    <tr>
+      <td align="center">Gemini-2.5-pro-deepresearch</td>
+      <td align="center">96.02</td>
+      <td align="center">90.71</td>
+      <td align="center">99.90</td>
+      <td align="center">93.37</td>
+      <td align="center">99.69</td>
+      <td align="center">95.00</td>
+      <td align="center">97.45</td>
+    </tr>
+    <tr>
+      <td align="center">WebWeaver (Qwen3-30b-a3b)</td>
+      <td align="center">77.27</td>
+      <td align="center">71.88</td>
+      <td align="center">85.51</td>
+      <td align="center">75.80</td>
+      <td align="center">84.78</td>
+      <td align="center">63.77</td>
+      <td align="center">81.88</td>
+    </tr>
+    <tr>
+      <td align="center">WebWeaver (Claude-sonnet-4)</td>
+      <td align="center">96.77</td>
+      <td align="center">90.50</td>
+      <td align="center">99.87</td>
+      <td align="center">94.30</td>
+      <td align="center">100.00</td>
+      <td align="center">98.73</td>
+      <td align="center">97.22</td>
+    </tr>
+    <tr>
+      <td align="center">AgentCPM-Report</td>
+      <td align="center">98.48</td>
+      <td align="center">95.1</td>
+      <td align="center">100.0</td>
+      <td align="center">98.5</td>
+      <td align="center">100.0</td>
+      <td align="center">97.3</td>
+      <td align="center">100.0</td>
+    </tr>
+  </tbody>
+</table>
+
+<table align="center">
+  <thead>
+    <tr>
+      <th align="center">DeepConsult</th>
+      <th align="center">Avg.</th>
+      <th align="center">Win</th>
+      <th align="center">Tie</th>
+      <th align="center">Lose</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td align="center">Doubao-research</td>
+      <td align="center">5.42</td>
+      <td align="center">29.95</td>
+      <td align="center">40.35</td>
+      <td align="center">29.7</td>
+    </tr>
+    <tr>
+      <td align="center">Claude-research</td>
+      <td align="center">4.6</td>
+      <td align="center">25</td>
+      <td align="center">38.89</td>
+      <td align="center">36.11</td>
+    </tr>
+    <tr>
+      <td align="center">OpenAI-deepresearch</td>
+      <td align="center">5</td>
+      <td align="center">0</td>
+      <td align="center">100</td>
+      <td align="center">0</td>
+    </tr>
+    <tr>
+      <td align="center">Gemini-2.5-Pro-deepresearch</td>
+      <td align="center">6.7</td>
+      <td align="center">61.27</td>
+      <td align="center">31.13</td>
+      <td align="center">7.6</td>
+    </tr>
+    <tr>
+      <td align="center">WebWeaver(Qwen3-30B-A3B)</td>
+      <td align="center">4.57</td>
+      <td align="center">28.65</td>
+      <td align="center">34.9</td>
+      <td align="center">36.46</td>
+    </tr>
+    <tr>
+      <td align="center">WebWeaver(Claude-Sonnet-4)</td>
+      <td align="center">6.96</td>
+      <td align="center">66.86</td>
+      <td align="center">10.47</td>
+      <td align="center">22.67</td>
+    </tr>
+    <tr>
+      <td align="center">Enterprise-DR(Gemini-2.5-Pro)</td>
+      <td align="center">6.82</td>
+      <td align="center">71.57</td>
+      <td align="center">19.12</td>
+      <td align="center">9.31</td>
+    </tr>
+    <tr>
+      <td align="center">RhinoInsigh(Gemini-2.5-Pro)</td>
+      <td align="center">6.82</td>
+      <td align="center">68.51</td>
+      <td align="center">11.02</td>
+      <td align="center">20.47</td>
+    </tr>
+    <tr>
+      <td align="center">AgentCPM-Report</td>
+      <td align="center">6.6</td>
+      <td align="center">57.6</td>
+      <td align="center">13.73</td>
+      <td align="center">28.68</td>
+    </tr>
+  </tbody>
+</table>
+
+Our evaluation datasets include DeepResearch Bench, DeepConsult, and DeepResearch Gym. The writing-time knowledge base includes about 2.7 million [Arxiv papers](https://www.kaggle.com/api/v1/datasets/download/Cornell-University/arxiv) and about 200,000 internal webpage summaries.
+
+## Acknowledgements
+This project would not be possible without the support and contributions of the open-source community. During development, we referred to and used multiple excellent open-source frameworks, models, and data resources, including [verl](https://github.com/volcengine/verl), [UltraRAG](https://github.com/OpenBMB/UltraRAG), [MiniCPM4.1](https://github.com/OpenBMB/MiniCPM4.1), and [SurveyGo](https://surveygo.modelbest.cn/).
+
+## Contributions
+Project leads: Yishan Li, Wentong Chen
+
+Contributors: Yishan Li, Wentong Chen, Yukun Yan, Mingwei Li, Sen Mei, Xiaorong Wang, Kunpeng Liu, Xin Cong, Shuo Wang, Zhong Zhang, Yaxi Lu, Zhenghao Liu, Yankai Lin, Zhiyuan Liu, Maosong Sun
+
+Advisors: Yukun Yan, Yankai Lin, Zhiyuan Liu, Maosong Sun
+
+## Citation
+
+If **AgentCPM-Report** is helpful for your research, please cite it as follows:
+
+```bibtex
+@software{AgentCPMReport2026,
+  title  = {AgentCPM-Report: Gemini-2.5-pro-DeepResearch Level Local DeepResearch},
+  author = {Yishan Li, Wentong Chen, Yukun Yan, Mingwei Li, Sen Mei, Xiaorong Wang, Kunpeng Liu, Xin Cong, Shuo Wang, Zhong Zhang, Yaxi Lu, Zhenghao Liu, Yankai Lin, Zhiyuan Liu, Maosong Sun},
+  year   = {2026},
+  url    = {https://github.com/OpenBMB/AgentCPM}
+}
+```