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cs.AI2026
Sparse MLLM Anchors, Dense Adaptation: Breaking the Self-Referential Loop in Wild Test-Time Adaptation
Zhenbin Wang, Lei Zhang, Lituan Wang +3
Wild test-time adaptation (WTTA) updates a source model online under small test batches, concurrent distribution shifts, and time-varying class imbalance. Most WTTA methods derive…
cs.CV2026
StackTok: Accelerating VLMs Inference with Budget-Adaptive Visual Token Selection
Zhenbin Wang, Lei Zhang, Lituan Wang +3
Increasing image resolution produces ever-longer visual-token sequences in vision-language models (VLMs), substantially raising their inference cost. To reduce this overhead withou…
cs.AI2026
Layers, Sinks, and Scaling: Adaptive Evidence Selection for Multimodal Large Language Models
Zhenbin Wang, Lei Zhang, Lituan Wang +3
Multimodal large language models (MLLMs) can answer knowledge-intensive visual questions by combining visual evidence from images with facts retrieved from external sources. Howeve…