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cs.CV2026
Self-Captioning Multimodal Interaction Tuning: Amplifying Exploitable Redundancies for Robust Vision Language Models
Yuriel Ryan, Hei Man Ip, Adriel Kuek +2
Current vision language models face hallucination and robustness issues against ambiguous or corrupted modalities. We hypothesize that these issues can be addressed by exploiting t…
cs.CV2025
TAMP: Token-Adaptive Layerwise Pruning in Multimodal Large Language Models
Jaewoo Lee, Keyang Xuan, Chanakya Ekbote +3
Multimodal Large Language Models (MLLMs) have shown remarkable versatility in understanding diverse multimodal data and tasks. However, these capabilities come with an increased mo…