NEXUS-O

<p align="center"> <h1 align="center">NEXUS-O: An Omni-Perceptive And -Interactive Model for Language, Audio, And Vision</h1> <p align="center"> <strong>Che Liu</strong> , <strong>Yingji Zhang</strong> , <strong>Dong Zhang</strong> , <strong>Weijie Zhang</strong> , <strong>Chenggong Gong</strong> , <strong>Yu Lu</strong> , <strong>Shilin Zhou</strong> , <strong>Ziliang Gan</strong> , <br> <strong>Ziao Wang</strong>, <strong>Haipang Wu</strong>, <strong>Ji Liu</strong>, <strong>Andre Freitas</strong>, <strong>Qifan Wang</strong>, <strong>Zenglin Xu</strong>, <br> <strong>Rongjunchen Zhang</strong>^♠, <strong>Yong Dai</strong>^♠ </p> <div class="is-size-5 publication-authors" align="center"> <span class="author-block"> ^♠Corresponding author, daiyongya@outlook.com, zhangrongjunchen@myhexin.com </span> </div> <br> 📖<a href="https://arxiv.org/pdf/2503.01879">Paper</a> |🤗<a href="https://huggingface.co/HiThink-Research/NEXUS-O">Model</a></h3> | 🤗<a href="https://huggingface.co/HiThink-Research/NEXUS-O">Training Data (Coming Soon)</a></h3> <div align="center"></div> <p align="center"> <p> NEXUS-O is an industry-scale omni-modal large language model (LLM) that unifies audio, vision, and language understanding into a single modular framework. Human perception integrates sight, sound, and language — NEXUS-O aims to replicate this ability for intelligent agents across real-world scenarios such as ASR, Speech-to-Speech Chat, and Multimodal Reasoning. </p> <img src="static/omni.png"> <p>Architecture of NEXUS-O</p> <img src="static/train_stage.png"> <p>Training Stages</p>

📢 News

• 🚀 [08/01/2025] Our paper has been accepted for ACM MM 2025.

💡 Highlights

• 🧩 Modular End-to-End Framework. A highly configurable encoder–LLM–decoder architecture supporting flexible modality combinations and rapid iteration for industry applications.
• 💡 Lightweight Alignment Strategy. Efficient audio–language pre-training built upon the state-of-the-art Qwen2.5-VL model — eliminating the need for costly vision pre-training while retaining strong tri-modal performance.
• 🎧 Synthetic Audio Data Pipeline. A scalable audio synthesis system that generates diverse, high-fidelity audio-text pairs from real-world scenes, enabling robust downstream ASR and S2S tasks.

TODO

• Rlease NEXUS-O full model weight on HuggingFace
• Rlease Audio Encoder Training Data
• Rlease Audio Decoder Training Data

✒️Citation

@article{liu2025nexus,
  title={Nexus: An Omni-Perceptive And-Interactive Model for Language, Audio, And Vision},
  author={Liu, Che and Zhang, Yingji and Zhang, Dong and Zhang, Weijie and Gong, Chenggong and Li, Haohan and Lu, Yu and Zhou, Shilin and Lu, Yue and Gan, Ziliang and others},
  journal={arXiv preprint arXiv:2503.01879},
  year={2025}
}

📄 License

Usage and License Notices: The data and code are intended and licensed for research use only. License: Attribution-NonCommercial 4.0 International It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use

💖 Acknowledgement