AI RESEARCH
Task-Related Token Compression in Multimodal Large Language Models from an Explainability Perspective
arXiv CS.CV
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ArXi:2506.01097v2 Announce Type: replace Existing Multimodal Large Language Models (MLLMs) process a large number of visual tokens, leading to significant computational costs and inefficiency. Instruction-related visual token compression nstrates strong task relevance, which aligns well with MLLMs ultimate goal of instruction following. Previous works generally assume that visual tokens achieve better vision-language alignment in the shallow layers of LLMs, which have led to task-related token compression being primarily applied in intermediate LLM layers.