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cs.CV2026
ReMAP: Restoring the Perceptual Cycle with Reasoning-Time Latent Visual Memory
Hao Jiang, Zhanyu Guo, Chenwei Wu +11
As multimodal large language models (MLLMs) reason for longer, attention to the initial visual input diminishes, weakening visual grounding. Visual memory reintroduces visual evide…
cs.CV2026
MiCo: Mutual Information Coverage Optimization through Semantic Erasure Modeling for Efficient MLLM Inference
Tinghao Wang, Yichen Guo, Qizhe Zhang +11
Multimodal large language models (MLLMs) have demonstrated impressive performance in multimodal understanding, but processing large numbers of visual tokens results in high computa…
cs.CV2026
STAR-Pro: Stage-Wise Token Adaptive Reduction with Progressive Refinement for Efficient Large Vision-Language Models
Yichen Guo, Tinghao Wang, Qizhe Zhang +15
Large vision-language models (LVLMs) achieve strong multimodal understanding, but the hundreds to thousands of visual tokens they process impose substantial computational overhead,…