<p align="center"> <img src="docs/NEO_logo.jpg" style="vertical-align: -10px;" :height="40px" width="40px"> NEO Series: Native Vision-Language Models </p>

<p align="center"> <img src="docs/NEO_architecture.jpg" width="80%"> </p>

• 2025/09: From Pixels to Words -- Towards Native Vision-Language Primitives at Scale (ICLR 2026)

📜 News

[2026/01] 🔥🔥🔥 The training code of NEO is released !
[2025/10] The paper, weights, and evaluation code of NEO are released !
[2025/09] 💥💥💥 NEO has been completed !

📋 Todo List

• [x] Evaluation guide
• [x] Training guide

💡 Motivation

<p align="center"> <img src="docs/NEOv1_motivation.jpg"> </p>

• What constraints set native VLMs apart from modular ones, and to what extent can they be overcome?

• How to make native VLMs more accessible and democratized, thereby accelerating their progress?

💡 Highlights

• 🔥 Native Architecture: NEO innovates a native VLM primitive that unifies pixel-word encoding, alignment, and reasoning within a dense, monolithic model architecture.

• 🔥 Superior Efficiency: With merely 390M image-text examples, NEO develops strong visual perception from scratch, rivaling top-tier modular VLMs and outperforming native ones.

• 🔥 Promising Roadmap: NEO pioneers a promising route for scalable and powerful native VLMs, paired with diverse reusable components that foster a cost-effective and extensible ecosystem.

🤖 Model Zoo

We release 2B and 9B NEO in Pre-Training (PT), Mid-Training (MT), and Supervised Fine-Tuning (SFT).

| Model Name | Model Weight| | ---------- | ------------------------------------------------------- | | NEO-2B-PT | 🤗 NEO-2B-PT HF link | | NEO-2B-MT | 🤗 NEO-2B-MT HF link | | NEO-2B-SFT | 🤗 NEO-2B-SFT HF link | | NEO-9B-PT | 🤗 NEO-9B-PT HF link | | NEO-9B-MT | 🤗 NEO-9B-MT HF link | | NEO-9B-SFT | 🤗 NEO-9B-SFT HF link |

📊 Benchmark Results

TABLE NOTE:

• “# Data” = data scale for pre-training / mid-training / supervised fine-tuning.
• “†“ = vision-language models using Reinforcement Learning (RL).
• “Any Res.” = any resolution; “Tile-wise” = image split into tiles; <br> “Any Rat.” = any aspect ratio; “Fix Res.” = fixed resolution.
• “MoE“ = Mixture-of-Experts; “DaC“ = Divide-and-Conquer.
• Bold = best score in each column. <br><br>

| Model_NAME | Base_LLM_NAME | #Data_PT·MT·SFT | Input_TYPE | RoPE_TYPE | MMMU | MMB | MMVet | MMStar | SEED_I | POPE | HallB | AI2D | DocVQA | ChartQA | InfoVQA | TextVQA | OCRBench | |:--|:--|:--|:--|:--|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:| | 🔻Modular_VLMs_(2B) ||||||||||||||||||| | Qwen2-VL | Qwen2-1.5B | --·--·-- | Any Res. | M-RoPE | 41.1 | 74.9 | 49.5 | 48.0 | -- | -- | 41.7 | 74.7 | 90.1 | 73.5 | 65.5 | 79.7 | 80.9 | | InternVL2.5 | InternLM2.5-1.8B | >6B·100M·16M | Tile-wise | 1D-RoPE | 43.6 | 74.7 | 60.8 | 53.7 | -- | 90.6 | 42.6 | 74.9 | 88.7 | 79.2 | 60.9 | 74.3 | 80.4 | | InternVL3† | Qwen2.5-1.5B | >6B·100M·22M | Tile-wise | 1D-RoPE | 48.6 | 81.1 | 62.2 | 60.7 | -- | 89.6 | 42.5 | 78.7 | 88.3 | 80.2 | 66.1 | 77.0 | 83.5 | | Qwen2.5-VL† | Qwen2.5-3B | --·--·-- | Any Res. | M-RoPE | 51.2 | 79.1 | 61.8 | 55.9 | -- | -- | 46.3 | 81.6 | 93.9 | 84.0 | 77.1 | 79.3 | 79.7 | | Encoder_Based | Qwen3-1.7B | >6B·40M·4M | Tile-wise | 1D-RoPE | 47.1 | 75.8 | 37.4 | 52.7 | 73.6 | 87.0 | 44.4 | 77.4 | 89.9 | 78.4 | 65.9 | 73.3 | 83.5 | | 🔻Native_VLMs_(2B) ||||||||||||||||||| | Mono-InternVL | InternLM2-1.8B | 1.2B·143M·7M | Tile-wise | 1D-RoPE | 33.7 | 65.5 | 40.1 | -- | 67.4 | -- | 34.8 | 68.6 | 80.0 | 73.7 | 43.0 | 72.6 | 76.7 | | Mono-InternVL-1.5 | InternLM2-1.8B | 400M·150M·7M | Tile-wise | 1D-RoPE | 39.1 | 64.0 | 54.0 | -- | 66.9 | -- | 32.5 | 67.4 | 81.7 | 72.2 | 47.9 | 73.7 | 80.1 | | HoVLE | InternLM2-1.8B | 550M·50M·7M | Tile-wise | 1D-RoPE | 32.2 | 73.3 | 43.8 | -- | 70.9 | 87.4 | 38.4 | 73.0 | 86.1 | 78.6 | 55.7 | 70.9 | 74.0 | | OneCAT | Qwen2.5-1.5B | 436M·70M·13M | Any Res. | M-RoPE | 39.0 | 72.4 | 42.4 | -- | 70.9 | -- | -- | 72.4 | 87.1 | 76.2 | 56.3 | 67.0 | -- | | NEO | Qwen3-1.7B | 345M·40M·4M | Any Res. | Native_RoPE | 48.6 | 76.0 | 49.6 | 54.2 | 74.2 | 87.5 | 43.1 | 80.1 | 89.9 | 81.2 | 63.2 | 74.0 | 77.1 |

| Model_NAME | Base_LLM_NAME | #Data_PT·MT·SFT | Input_TYPE | RoPE_TYPE | MMMU | MMB | MMVet | MMStar | SEED_I | POPE | HallB | AI2D | DocVQA | ChartQA | InfoVQA | TextVQA | OCRBench | |:--|:--|:--|:--|:--|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:| | 🔻Modular_VLMs_(8B) ||||||||||||||||||| | Qwen2-VL | Qwen2-7B | --·--·-- | Any Res. | M-RoPE | 54.1 | 83.0 | 62.0 | 60.7 | -- | 88.1 | 50.6 | 83.0 | 94.5 | 83.0 | 76.5 | 84.3 | 86.6 | | InternVL2.5 | InternLM2.5-7B | >6B·50M·4M | Tile-wise | 1D-RoPE | 56.0 | 84.6 | 62.8 | 64.4 | -- | 90.6 | 50.1 | 84.5 | 93.0 | 84.8 | 77.6 | 79.1 | 82.2 | | Qwen2.5-VL† | Qwen2.5-7B | --·--·-- | Any Res. | M-RoPE | 55.0 | 83.5 | 67.1 | 63.9 | -- | 86.4 | 52.9 | 83.9 | 95.7 | 87.3 | 82.6 | 84.9 | 86.4 | | InternVL3† | Qwen2.5-7B | >6B·100M·22M | Tile-wise | 1D-RoPE | 62.7 | 83.4 | 81.3 | 68.2 | -- | 91.1 | 49.9 | 85.2 | 92.7 | 86.6 | 76.8 | 80.2 | 88.0 | | Encoder-Based | Qwen3-8B | >6B·40M·4M | Tile-wise | 1D-RoPE | 54.1 | 84.0 | 60.0 | 63.5 | 76.2 | 87.8 | 51.4 | 82.9 | 92.1 | 83.5 | 75.0 | 77.1 | 85.3 | | 🔻Native_VLMs_(8B) ||||||||||||||||||| | Fuyu | Persimmon-8B | --·--·-- | Any Res. | 1D-RoPE | 27.9 | 10.7 | 21.4 | -- | 59.3 | 84.0 | -- | 64.5 | -- | -- | -- | -- | 36.6 | | Chameleon | from scratch | 1.4B·0M·1.8M | Fix Res. | 1D-RoPE | 25.4 | 31.1 | 8.3 | -- | 30.6 | 19.4 | 17.1 | 46.0 | 1.5 | 2.9 | 5.0 | 4.8 | 0.7 | | EVE | Vicuna-7B | 33M·0M·1.8M | Any Rat. | 1D-RoPE | 32.6 | 52.3 | 25.7 | -- | 64.6 | 85.0 | 26.4 | 61.0 | 53.0 | 59.1 | 25.0 | 56.8 | 39.8 | | SOLO | Mistral-7B | 44M·0M·2M | Any Res. | 1D-RoPE | -- | 67.7 | 30.4 | -- | 64.4 | 78.6 | -- | 61.4 | -- | -- | -- | -- | 12.6 | | Emu3 | from scratch | --·--·-- | Fix Res. | 1D-RoPE | 31.6 | 58.5 | 37.2 | -- | 68.2 | 85.2 | -- | 70.0 | 76.3 | 68.6 | 43.8 | 64.7 | 68.7 | | EVEv2 | Qwen2.5-7B | 77M·15M·7M | Any Rat. | 1D-RoPE | 39.3 | 66.3 | 45.0 | -- | 71.4 | 87.6 | -- | 74.8 | -- | 73.9 | -- | 71.1 | 70.2 | | BREEN | Qwen2.5-7B | 13M·0M·4M | Any Res. | 1D-RoPE | 42.7 | 71.4 | 38.9 | 51.2 | -- | -- | 37.0 | 76.4 | -- | -- | -- | 65.7 | -- | | VoRA | Qwen2.5-7B | 30M·0M·0.6M | Any Res. | 1D-RoPE | 32.0 | 61.3 | 33.7 | -- | 68.9 | 85.5 | -- | 61.1 | -- | -- | -- | 58.7 | -- | | SAIL | Mistral-7B | 512M·86M·6M | Any Res. | M-RoPE | -- | 70.1 | 46.3 | 53.1 | 72.9 | 85.8 | 54.2 | 76.7 | -- | -- | -- | 77.1 | 78.3 | | NEO | Qwen3-8B | 345M·40M·4M | Any Res. | Native_RoPE | 54.6 | 82.1 | 53.6 | 62.4 | 76.3 | 88.4 | 46.4 | 83.1 | 88.6 | 82.1 | 60.9 | 75.0 | 77.7 |

✒️ Citation

If NEO series is helpful for your research, please consider star ⭐ and citation 📝 :

@article{Diao2025NEO,
  title        = {From Pixels to Words--Towards Native Vision-Language Primitives at Scale},
  author       = {Diao, Haiwen and Li, Mingxuan and Wu, Silei and Dai, Linjun and Wang, Xiaohua and Deng, Hanming and Lu, Lewei and Lin, Dahua and Liu, Ziwei},
  journal      = {arXiv preprint arXiv:2510.14979},
  year         = {2025}
}

📄 License

The content of this project itself is licensed under LICENSE.