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University of Electronic Science and Technology of China
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Skewed item distributions in recommendation systems can be tamed with a learnable non-uniform quantization, leading to better codebook utilization and more accurate generative recommendations.
Forget blindly aligning layers in audio Flow Matching: AG-REPA reveals that targeting the *causally dominant* layers driving the velocity field, not just representationally rich ones, unlocks superior generation.
Generative recommendation can beat DLRM in large-scale advertising, driving a 4.2% revenue lift in Kuaishou's production system via innovations in tokenization, decoding, optimization, and serving.
