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Multi-Head Latent Attention (MLA) enables expressive multi-head attention with compact caches for its content and decoupled RoPE paths, yet cache memory still scales linearly with context length and batch size. In this work, we establish a systematic model of MLA's dual-path quantization errors, characterizing their di...
A cross-modal structural sensitivity asymmetry in VLMs is revealed and SeGO is proposed, a unified structural sensitivity-aware sparse optimization framework that achieves the balance among model parameter amount, quantization accuracy and scaling factors’ search efficiency on InternVL2 and LLaVA series.
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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