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LLM memory evolution reveals a shift from isolated attention to coordinated, multidimensional memory routing across depth and substrate, challenging the focus on single operators.
Standard visual token pruning targets the wrong encoder depth: tapping into intermediate representations while asymmetrically skipping decoder sub-layers cuts MLLM inference FLOPs by 79% without requiring any retraining.
