The paper introduces Omni-Interactive Universal Embedder (OmniUE), a unified embedding framework that learns a single representation space for text, video, and audio using learnable tokens and intermediate-layer representations. OmniUE supports omni-interactive querying, allowing users to input text, visual regions, or audio spans, which are processed by segmenters and an omni-LLM to generate user-conditioned embeddings. The authors evaluate OmniUE on the new OmniCHOIR benchmark and other multimodal tasks, reporting significant performance gains over state‑of‑the‑art baselines across textual, audio, and visual interactive settings.
By Wei-Yao Wang, Kazuya Tateishi, Shuyang Cui, Christian Simon, Takashi Shibuya, Shusuke Takahashi, Yuki Mitsufuji
arXiv:2606. 00959v1 Announce Type: new Abstract: Understanding modality interaction in multimodal large language models (MLLMs) is central to reliable deployment.
By Wanlong Fang, Tianle Zhang, Wen Tao, Alvin Chan
arXiv:2610.00576v1 Announce Type: new
Abstract: In this paper, we propose Gestalt, a new paradigm of large multimodal model built around multimodal interplay. Despite rapid advances, large multimodal...
By Zequn Yang, Yu Miao, Haotian Ni, Ziheng Chen, Chengxiang Huang, Dongzhan Zhou, Kai Chen, Qi Zhang, Ji-Rong Wen, Yake Wei, Di Hu
arXiv:2609.18323v1 Announce Type: new
Abstract: Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio. MiniMax-H...
By Haoyu Zhao, Zihao Zhao, Tianyu Deng, Ziqin Xu, Zihao Zhang, Xudong Wang, Jinxiang Guo, Chen Gao, Ziyi Ye, Yeying Jin, Jiaxi Gu, Zuxuan Wu, Shuicheng Yan
arXiv:2607. 10299v1 Announce Type: new Abstract: Recent advances in large-scale multimodal models have drivenremarkable progress in vision-language tasks; however, comprehensiveomni-modal understanding remains under-explored, largely due to thescarcity of datasets with rich, explicitly aligned auditory cues.
By Kaiying Yan, Luoyi Sun, Xiao Zhou, Weidi Xie
arXiv:2607. 00293v1 Announce Type: cross Abstract: Achieving true artificial general intelligence requires foundation models capable of integrating new modalities without forgetting prior knowledge.
By Xiangyue Liu, Zijian Zhang, Miles Yang, Zhao Zhong, Liefeng Bo, Ping Tan