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This work regularizes the energy of each rank-one LoRA component, encouraging redundant components to vanish while preserving important ones, and proposes a principled rank-allocation method based on classical sparsity-inducing technique in signal processing and statistics.
This paper proposes the memory-native NTN (Mem-NTN) paradigm that leverages long-horizon contexts for memory-augmented system optimization and establishes a dual-memory architecture that distinguishes between physical memory representing the state of the world and digital memory encoding historical network experience.
FluxBin is proposed, an algorithm-kernel co-design that synergizes post-training quantization with a highly optimized CUDA kernel and introduces Decoupled Row-Column Binary Decomposition to enhance representational capacity while maintaining hardware efficiency.
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