Update README.md

README.md

@@ -28,6 +28,8 @@ The foundational Qwen Omni model ([Qwen/Qwen2.5-Omni-3B](https://huggingface.co/
 
 This model is for research and development only.
 
+For more technical details, please refer to our technical report: [Omni-Embed-Nemotron: A Unified Multimodal Retrieval Model for Text, Image, Audio, and Video](https://arxiv.org/abs/2510.03458)
+
 ### License/Terms of Use
 
 Governing Terms for nvidia/omni-embed-nemotron-3b model: [NVIDIA OneWay Noncommercial License.](https://huggingface.co/nvidia/omni-embed-nemotron-3b/blob/main/LICENSE)
@@ -52,6 +54,12 @@ Correspondence to Mengyao Xu (mengyaox@nvidia.com) and Benedikt Schifferer (bsch
 ### Citation
 
 ```
+@article{xu2025omni,
+  title={Omni-Embed-Nemotron: A Unified Multimodal Retrieval Model for Text, Image, Audio, and Video},
+  author={Xu, Mengyao and Zhou, Wenfei and Babakhin, Yauhen and Moreira, Gabriel and Ak, Ronay and Osmulski, Radek and Liu, Bo and Oldridge, Even and Schifferer, Benedikt},
+  journal={arXiv preprint arXiv:2510.03458},
+  year={2025}
+}
 @misc{moreira2025nvretrieverimprovingtextembedding,
       title={NV-Retriever: Improving text embedding models with effective hard-negative mining}, 
       author={Gabriel de Souza P. Moreira and Radek Osmulski and Mengyao Xu and Ronay Ak and Benedikt Schifferer and Even Oldridge},