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cs.CV2026
MM-Snowball: Evaluating and Mitigating Hallucination Snowballing in Multimodal Multi-Turn Dialogue
Yue Jiang, Xue Jiang, Lihua Zhang +6
Multimodal large language models (MLLMs) demonstrate remarkable visual understanding, yet their reliability in interactive settings is severely undermined by hallucination snowball…
cs.CV2026
Hard to Read, Easy to Jailbreak: How Visual Degradation Bypasses MLLM Safety Alignment
Zhixue Song, Boyan Han, Yiwei Wang +1
Recent advancements in visual context compression enable MLLMs to process ultra-long contexts efficiently by rendering text into images. However, we identify a critical vulnerabili…
cs.CV2026
ASAP: Attention-Shift-Aware Pruning for Efficient LVLM Inference
Surendra Pathak, Bo Han
While Large Vision-Language Models (LVLMs) demonstrate exceptional multi-modal capabilities, the quadratic computational cost of processing high-resolution visual tokens remains a…