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Achieving competitive performance in Vision-Language Models while reducing computational overhead could revolutionize their deployment on edge devices.
LLMs under attack exhibit a tell-tale "Entropy Lull" – a period of abnormally low and stable token probability entropy – that can be exploited for highly accurate and efficient attack detection.
