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StackTok: Accelerating VLMs Inference with Budget-Adaptive Visual Token Selection
StackTok is introduced, a training-free selector that treats query relevance as the objective and visual coverage as budget-calibrated support in vision-language models and ranks first among training-free selectors in every tested model--budget setting.
Sparse MLLM Anchors, Dense Adaptation: Breaking the Self-Referential Loop in Wild Test-Time Adaptation
MASA (Multimodal-LLM-Anchored Semantic Adaptation), which complements model-internal evidence with structured semantic descriptions from a frozen multimodal large language model (MLLM) to limit inference cost.