3 papers
cs.CL2025
NExT-OMNI: Towards Any-to-Any Omnimodal Foundation Models with Discrete Flow Matching
Run Luo, Xiaobo Xia, Lu Wang +5
Next-generation multimodal foundation models capable of any-to-any cross-modal generation and multi-turn interaction will serve as core components of artificial general intelligenc…
cs.CL2025
CLaSp: In-Context Layer Skip for Self-Speculative Decoding
Longze Chen, Renke Shan, Huiming Wang +6
Speculative decoding (SD) is a promising method for accelerating the decoding process of Large Language Models (LLMs). The efficiency of SD primarily hinges on the consistency betw…
cs.CL2025
VCM: Vision Concept Modeling Based on Implicit Contrastive Learning with Vision-Language Instruction Fine-Tuning
Run Luo, Renke Shan, Longze Chen +4
Large Vision-Language Models (LVLMs) are pivotal for real-world AI tasks like embodied intelligence due to their strong vision-language reasoning abilities. However, current LVLMs…