Yuhao Dong^*,1  Zuyan Liu^*,2,3  Hai-Long Sun^2,4  Jingkang Yang¹ 

Winston Hu²  Yongming Rao^2,3,✉  Ziwei Liu^1,✉ 

¹S-Lab, NTU   ²Tencent  ³Tsinghua University ⁴Nanjing University 

^* Equal Contribution^✉ Corresponding Author

arXiv Paper:

Model Checkpoints:

• [04/2025] Insight-V is selected as Highlight paper by CVPR2025!
• [02/2025] Insight-V is accepted by CVPR2025!
• [11/2024] 🔧🔨Training & Inference Scripts Release! Try Insight-V on your own!
• [11/2024] 🔥 🚀Introducing Insight-V! An early attempt to explore long-chain visual reasoning with MLLMs.
  • [Paper]: Detailed introduction of Insight-V, including structured, long-chain data generation pipeline and effective multi-agent system design!
  • [Checkpoints]: We release model checkpoints on LLaVA-NeXT-LLaMA3 and our base model.

Insight-V is an early effort to explore long-chain visual reasoning with MLLMs.

Insight-V offers 1) a scalable data generation pipeline for long-chain, high-quality reasoning data, 2) a multi-agent system that decomposes visual reasoning tasks into reasoning and summarization, and 3) a two-stage training pipeline to enhance visual reasoning capabilities. Together, these contributions address key challenges in visual reasoning, providing a solid foundation for future research in MLLM reasoning.

The reasoning processes are generated progressively through a reasoning generator, and then fed into a multi-granularity assessment system to ensure high-quality reasoning.

We derive a multi-agent system from a single model. By decomposing the task into reasoning and summarization, the two agents collaborate to enhance the overall reasoning capability.

• Release paper on arXiv
• Release Insight-V models.
• Demo code for generation.
• All the training and inference code.
• Evaluation code for visual reasoning benchmarks.
• Insight-V SFT Data.
• Insight-V with stronger MLLMs.

If you find it useful for your research and applications, please cite our paper using this BibTeX:

@article{dong2024insight,
  title={Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models},
  author={Dong, Yuhao and Liu, Zuyan and Sun, Hai-Long and Yang, Jingkang and Hu, Winston and Rao, Yongming and Liu, Ziwei},
  journal={arXiv preprint arXiv:2411.14432},
  year={2024}
}

• Our codebase is conducted on LLaVA

• The data generation pipeline is mitigated from g1

• Thanks to lmms-eval team, for building such a useful evaluation system!