3 citations · 4 across the 5 of their papers we have counts for
5 papers
Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs
Zhenyu Liu, Xuanyu Zhang, Yunxin Li +10
Developing seamless, high-performance, native intelligent full-duplex Spoken Language Models (SLMs) remains a critical challenge and long-standing goal for the speech and NLP commu…
Uni-MoE-2.0-Omni: Scaling Language-Centric Omnimodal Large Model with Advanced MoE, Training and Data
Yunxin Li, Xinyu Chen, Shenyuan Jiang +9
We present Uni-MoE 2.0 from the Lychee family. As a fully open-source omnimodal large model (OLM), it substantially advances Lychee's Uni-MoE series in language-centric multimodal…
UniMoE-Audio: Unified Speech and Music Generation with Dynamic-Capacity MoE
Zhenyu Liu, Yunxin Li, Xuanyu Zhang +13
Recent advances in unified multimodal models indicate a clear trend towards comprehensive content generation. However, the auditory domain remains a significant challenge, with mus…
Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models
Yunxin Li, Zhenyu Liu, Zitao Li +19
Reasoning lies at the heart of intelligence, shaping the ability to make decisions, draw conclusions, and generalize across domains. In artificial intelligence, as systems increasi…
Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts
Yunxin Li, Shenyuan Jiang, Baotian Hu +5
Recent advancements in Multimodal Large Language Models (MLLMs) underscore the significance of scalable models and data to boost performance, yet this often incurs substantial comp…