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cs.CV2026
Intern Lumina U2: A Multi-Codebook Diffusion Large Language Model for Omni-Visual Understanding and Image Generation
Qian Yu, Zongyang Qiu, Juncheng Yan +40
Unified multimodal generation covers discrete, continuous, and hybrid paradigms. Discrete approaches with single-codebook visual tokenizers align naturally with text modeling but h…
cs.CV2026
Q Cache: Visual Attention is Valuable in Less than Half of Decode Layers for Multimodal Large Language Model
Jiedong Zhuang, Lu Lu, Ming Dai +4
Multimodal large language models (MLLMs) are plagued by exorbitant inference costs attributable to the profusion of visual tokens within the vision encoder. The redundant visual to…
cs.CV2024
ST: Accelerating Multimodal Large Language Model by Spatial-Temporal Visual Token Trimming
Jiedong Zhuang, Lu Lu, Ming Dai +4
Multimodal large language models (MLLMs) enhance their perceptual capabilities by integrating visual and textual information. However, processing the massive number of visual token…