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LLMs break in two fundamentally different ways when pushed to extreme quantization: either through gradual information loss or sudden functional breakdown of key components.
Current OmniLLMs stumble when processing real-world, long-form audio-visual content, achieving only ~35-65% accuracy on a new benchmark designed to test long-term memory and fine-grained understanding.
