---
title: ExoMind
canonical_url: "https://www.modelscope.cn/models/AI4SGI/ExoMind"
md_url: "https://www.modelscope.cn/models/AI4SGI/ExoMind.md"
repository: AI4SGI/ExoMind
last_updated: 2026-09-01
license: apache-2.0
pipeline_tag: image-text-to-text
tasks:
- image-text-to-text
model_type:
- qwen3_5_moe
architectures:
- Qwen3_5MoeForConditionalGeneration
base_model:
- Qwen/Qwen3.5-35B-A3B
base_model_relation: finetune
parameters: 36.0B
tensor_type:
- BF16
library_name:
- safetensors
language:
- en
- zh
downloads: 37
stars: 0
tags:
- exomind
- scientific-reasoning
- scientific-research
- agentic
- tool-use
- multimodal
- vision-language
- qwen3.5
- safetensors
---
# ExoMind
> ExoMind - AI4SGI 在 ModelScope 开源的模型。ExoMind: Democratizing Scientific Intelligence via Extended-Mind-Inspired Agentic System
AI4SGI/ExoMind 是 ModelScope 魔搭社区上的 36.0B 参数image-text-to-text模型，采用 apache-2.0 许可，基于 Qwen/Qwen3.5-35B-A3B 构建。
- **Repository**: AI4SGI/ExoMind
- **License**: apache-2.0
- **Tasks**: image-text-to-text
- **Parameters**: 36.0B
- **Base model**: Qwen/Qwen3.5-35B-A3B
- **Tags**: exomind, scientific-reasoning, scientific-research, agentic, tool-use, multimodal, vision-language, qwen3.5, safetensors
- **Downloads**: 37
- **Stars**: 0
- **Last updated**: 2026-09-01
Source: https://www.modelscope.cn/models/AI4SGI/ExoMind
---
## 🔥 News
- **2026-08-28**: 🔥 The ExoMind preprint is now available on
[Preprints.org](https://doi.org/10.20944/preprints202608.2038.v1).
- **2026.8.25**: 🔥 We have released a series of official GGUF variants for
ExoMind and ExoMind-9B. Please refer to the [ExoMind collection](https://huggingface.co/collections/AI4SGI/exomind-6a8d093b41eedd517bbc0945). We
also thank [mradermacher](https://huggingface.co/mradermacher) for providing
additional GGUF quantizations, including both static and importance-matrix
variants: [ExoMind-i1-GGUF](https://huggingface.co/mradermacher/ExoMind-i1-GGUF),
[ExoMind-GGUF](https://huggingface.co/mradermacher/ExoMind-GGUF),
[ExoMind-9B-i1-GGUF](https://huggingface.co/mradermacher/ExoMind-9B-i1-GGUF),
and [ExoMind-9B-GGUF](https://huggingface.co/mradermacher/ExoMind-9B-GGUF).
- **2026-08-12**: We release the official project page and public repository.
## Overview
ExoMind is the first extended-mind-inspired agentic system designed for
scientific reasoning and research. It organizes a general-purpose model,
specialized interaction objects, and autonomous interaction processes as one
system, allowing the model to discover sources, ground evidence, execute
verification, and update its reasoning around each scientific problem.
This repository hosts the main checkpoint, fine-tuned from
[Qwen3.5-35B-A3B](https://huggingface.co/Qwen/Qwen3.5-35B-A3B). With
training-value-aware data engineering, a scientific interaction framework, and
two-stage progressive Chain-of-Interaction training, ExoMind raises the average
score across eight scientific benchmarks from **36.2 to 68.3**, achieves the
highest average among all evaluated models, and ranks first on six benchmarks.
## Highlights
- **Extended-mind-inspired intelligence:** unifies the LLM, interaction
objects, and autonomous interaction processes as a scientific agentic system.
- **Training-value-aware data engineering:** identifies challenging, learnable
problems and routes them to pure-reasoning or interaction-reasoning data.
- **Scientific interaction:** turns source discovery, evidence grounding,
executable verification, and observation integration into composable objects.
- **Progressive CoI training:** jointly develops intrinsic reasoning and
autonomous interaction using a few thousand high-quality trajectories.
- **Efficient frontier performance:** completes two-stage full-parameter SFT in
1–2 days on 8 NVIDIA H200 GPUs while improving all six evaluated general
capability benchmarks over the base model.
## Quick Start
Use a recent vLLM or SGLang release with Qwen3.5 support. The examples below
use the checkpoint's configured maximum context length of 262,144 tokens.
### vLLM
```bash
vllm serve AI4SGI/ExoMind \
--port 8000 \
--tensor-parallel-size 8 \
--max-model-len 262144 \
--reasoning-parser qwen3 \
--enable-auto-tool-choice \
--tool-call-parser qwen3_coder
```
### SGLang
```bash
python -m sglang.launch_server \
--model-path AI4SGI/ExoMind \
--host 0.0.0.0 \
--port 8000 \
--tp-size 8 \
--context-length 262144 \
--reasoning-parser qwen3 \
--tool-call-parser qwen3_coder
```
### OpenAI-Compatible API
```python
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
response = client.chat.completions.create(
model="AI4SGI/ExoMind",
messages=[
{
"role": "user",
"content": "Develop and verify a rigorous solution to this scientific problem: ...",
}
],
temperature=1.0,
top_p=0.95,
extra_body={"top_k": 20},
)
print(response.choices[0].message.content)
```
The complete scientific interaction workflow and prompt contracts are available
in the [ExoMind GitHub repository](https://github.com/AI4SGI/ExoMind).
## Evaluation
Under the preprint's evaluation setup, ExoMind reaches an
eight-benchmark average of **68.3**, compared with **54.2** for the next-best
representative model shown below.
🥇 Best score among the representative models shown
| Benchmark |
⭐ Ours |
Representative frontier models |
ExoMind
35B-A3B |
Claude-Opus-4.8
Thinking |
GPT-5.5
(xhigh) |
Gemini-3.1-Pro
Preview |
Kimi-K3 |
Qwen3.7-Max |
GLM-5.2 |
DeepSeek-V4-Pro
(Max) |
| 🧪 Scientific Research |
| HLE w/ tools | 56.8 | 🥇 57.9 | 52.2 | 51.4 | 56.0 | 53.5 | 54.7 | 48.2 |
| FrontierScience-Research | 🥇 70.0 | 26.7 | 26.7 | 11.7 | 21.7 | 10.0 | 15.0 | 13.3 |
| CMT-Benchmark | 🥇 84.0 | 46.0 | 43.0 | 43.0 | 34.0 | 34.0 | 20.0 | 28.0 |
| CritPt | 25.7 | 20.9 | 🥇 27.1 | 17.7 | 23.4 | 13.4 | 20.9 | 7.1 |
| 🧠 Scientific Reasoning |
| AMO-Bench | 🥇 78.0 | 74.0 | 70.0 | 63.1 | 64.0 | 57.4 | 54.0 | 68.0 |
| IMO-AnswerBench | 🥇 92.8 | 86.8 | 83.8 | 90.0 | 82.8 | 90.0 | 91.0 | 89.8 |
| HiPhO | 🥇 49.7 | 46.4 | 43.3 | 43.4 | 42.4 | 38.8 | 37.4 | 38.7 |
| FrontierScience-Olympiad | 🥇 89.0 | 75.0 | 78.0 | 77.0 | 69.0 | 80.0 | 76.5 | 76.0 |
| Eight-benchmark average | 🥇 68.3 | 54.2 | 53.0 | 49.7 | 49.2 | 47.1 | 46.2 | 46.1 |
See the [interactive evaluation
explorer](https://ai4sgi.github.io/ExoMind/#results) for the complete model list,
benchmark scopes, settings, and rankings.
## Intended Use
ExoMind is intended for research and development in scientific question
answering, literature-grounded investigation, mathematical and computational
reasoning, code-assisted verification, and agentic scientific workflows.
## License and Attribution
The distributed checkpoint and upstream Qwen3.5 materials are provided under
the Apache License 2.0 included in this repository. The preprint,
scientific figures and results, and ExoMind brand assets are subject to the
[ExoMind Research Content and Brand Terms](./CONTENT_RIGHTS.md). See
[NOTICE.md](./NOTICE.md) for third-party notices.
## Citation
```bibtex
@article{Ye_2026,
title = {ExoMind: Democratizing Scientific Intelligence via Extended-Mind-Inspired Agentic System},
author = {Ye, Peng and Liu, Zhuo and Ye, Jingqi and Yu, Fangchen and Tang, Shengji and Jiang, Yichen and He, Haonan and Cao, Zongsheng and Chen, Tao and Zhang, Bo and Ouyang, Wanli and Zhou, Bowen and Bai, Lei},
year = {2026},
month = aug,
publisher = {MDPI AG},
doi = {10.20944/preprints202608.2038.v1},
url = {https://doi.org/10.20944/preprints202608.2038.v1}
}
```
# ExoMind: Democratizing Scientific Intelligence via Extended-Mind-Inspired Agentic System
**ExoMind Team · Shanghai Artificial Intelligence Laboratory**
