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It is found that expert selection is far more redundant than the field's operating points assume, and how much expert computation fine-grained MoEs can dispense with is revealed, and when dynamic allocation earns its complexity is established, informing both practical deployment and future pruning methods.
Achieving extreme low-bit compression without lookup tables could revolutionize how we deploy large language models in memory-limited settings.
LongSpace reveals that integrating explicit spatial memory into MLLMs can dramatically enhance their performance on long-horizon tasks, a critical advancement for applications in autonomous navigation.
