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Model: openbmb/AgentCPM-Explore
Source: Original Platform
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ModelHub XC
2026-05-29 18:43:12 +08:00
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README.md Normal file
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---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-4B-Thinking-2507
library_name: transformers
---
## Latest News
* [2026-01-12]🚀🚀🚀 We have open-sourced **AgentCPM-Explore**, an agent foundation model with only **4B parameters**, together with its **entire training and inference infrastructure**. AgentCPM-Explore has successfully entered **8 classic long-horizon agent benchmarks**, including **GAIA,HLE, and BrowserComp**. AgentCPM-Explore achieves **SOTA performance at the same parameter scale** and demonstrates its **accurate deep research capabilities**, effectively breaking the performance bottleneck for **on-device agents**.
## Overview
Key highlights of AgentCPM-Explore include:
- The **first full-parameter 4B agent model** to rank on **8 long-horizon and complex agent benchmarks**, including **GAIA, HLE, and BrowserComp**, in the on-device setting.
- Capable of **over 100 rounds of continuous environment interaction**, supporting **multi-source information cross-validation**, **dynamic search strategy adjustment**, and **real-time verification of up-to-date information**, enabling sustained deep exploration until task completion.
- **Fully open-sourced end-to-end**, including (1) **AgentRL**, a fully asynchronous reinforcement learning framework for agent training, (2) **AgentDock**, a unified management and scheduling platform for tool sandboxes, (3) **AgentToLeaP**, a one-click evaluation platform for agent tool-learning capabilities. These components collectively support **community collaboration and custom extensibility**.
We elaborate on the entire construction pipeline of AgentCPM-Explore on [GitHub](https://github.com/OpenBMB/AgentCPM).
## Experimental Results
<table>
<thead>
<tr>
<th>Model</th>
<th>GAIA (text-only)</th>
<th>BrowseComp</th>
<th>BrowseComp (ZH)</th>
<th>HLE</th>
<th>Frames</th>
<th>WebWalker</th>
<th>Seal-0</th>
<th>Xbench-DeepSearch</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="9"><strong>Closed-Source Models</strong></td>
</tr>
<tr>
<td>Claude-4.5-sonnet</td>
<td>71.2%</td>
<td>19.6%</td>
<td>40.8%</td>
<td>24.5%</td>
<td>85.0%</td>
<td>/</td>
<td>53.4%</td>
<td>66.0%</td>
</tr>
<tr>
<td>Gemini Deep Research</td>
<td>/</td>
<td>/</td>
<td>/</td>
<td>26.9%</td>
<td>/</td>
<td>/</td>
<td>/</td>
<td>/</td>
</tr>
<tr>
<td>DeepSeek-V3.2</td>
<td>63.5%</td>
<td>67.6%</td>
<td>65.0%</td>
<td>40.8%</td>
<td>80.2%</td>
<td>/</td>
<td>38.5%</td>
<td>71.0%</td>
</tr>
<tr>
<td>MiniMax-M2</td>
<td>75.7%</td>
<td>44.0%</td>
<td>48.5%</td>
<td>31.8%</td>
<td>/</td>
<td>/</td>
<td>/</td>
<td>72.0%</td>
</tr>
<tr>
<td>OpenAI-GPT-5-high</td>
<td>76.4%</td>
<td>54.9%</td>
<td>65.0%</td>
<td>35.2%</td>
<td>/</td>
<td>/</td>
<td>51.4%</td>
<td>77.8%</td>
</tr>
<tr>
<td>GLM-4.6</td>
<td>71.9%</td>
<td>45.1%</td>
<td>49.5%</td>
<td>30.4%</td>
<td>/</td>
<td>/</td>
<td>/</td>
<td>70.0%</td>
</tr>
<tr>
<td>Kimi-Researcher</td>
<td>/</td>
<td>/</td>
<td>/</td>
<td>26.9%</td>
<td>78.8%</td>
<td>/</td>
<td>36.0%</td>
<td>69.0%</td>
</tr>
<tr>
<td>Seed-1.8</td>
<td>87.4%</td>
<td>67.6%</td>
<td>81.3%</td>
<td>40.9%</td>
<td>/</td>
<td>/</td>
<td>/</td>
<td>/</td>
</tr>
<tr>
<td colspan="9"><strong>Open-Source Models</strong></td>
</tr>
<tr>
<td>MiroThinker 8B</td>
<td>66.4%</td>
<td>31.1%</td>
<td>40.2%</td>
<td>21.5%</td>
<td>80.6%</td>
<td>60.6%</td>
<td>40.4%</td>
<td>60.6%</td>
</tr>
<tr>
<td>Tongyi DeepResearch 30B</td>
<td>70.9%</td>
<td>43.4%</td>
<td>46.7%</td>
<td>32.9%</td>
<td>90.6%</td>
<td>72.2%</td>
<td>/</td>
<td>75.0%</td>
</tr>
<tr>
<td>ASearcher QWQ 32B v2</td>
<td>58.7%</td>
<td>/</td>
<td>/</td>
<td>/</td>
<td>74.5%</td>
<td>/</td>
<td>/</td>
<td>51.1%</td>
</tr>
<tr>
<td>iterresearch-30B-A3B</td>
<td>72.8%</td>
<td>37.3%</td>
<td>45.2%</td>
<td>28.8%</td>
<td>71.0%</td>
<td>/</td>
<td>39.6%</td>
<td>/</td>
</tr>
<tr>
<td>WebSailor-V2-30B-A3B (RL)</td>
<td>74.1%</td>
<td>35.3%</td>
<td>44.1%</td>
<td>30.6%</td>
<td>/</td>
<td>/</td>
<td>/</td>
<td>73.7%</td>
</tr>
<tr>
<td>WebLeaper-30B-A3B-RUC</td>
<td>73.2%</td>
<td>38.8%</td>
<td>/</td>
<td>/</td>
<td>/</td>
<td>/</td>
<td>48.6%</td>
<td>72.0%</td>
</tr>
<tr>
<td>WebDancer (QWQ-32B)</td>
<td>51.5%</td>
<td>3.8%</td>
<td>18.0%</td>
<td>/</td>
<td>/</td>
<td>47.9%</td>
<td>/</td>
<td>38.3%</td>
</tr>
<tr>
<td><strong>AgentCPM-Explore 4B</strong></td>
<td>63.9%</td>
<td>25.0%</td>
<td>29.0%</td>
<td>19.1%</td>
<td>82.7%</td>
<td>68.1%</td>
<td>40.0%</td>
<td>70.0%</td>
</tr>
</tbody>
</table>

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
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{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
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{%- endif %}
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{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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