DeRes decouples residual stability and adaptivity via identity and block-attention paths with SiLU pointwise attention, delivering up to 0.32% AUC gains and steeper scaling laws on industrial and public CTR datasets.
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2026 2verdicts
UNVERDICTED 2representative citing papers
UniFormer introduces a unified model-centric scaling approach for recommender systems via feature-space and task-space modules, semantic tokenization, and multi-sequence attention, with reported gains in production A/B tests at Kuaishou.
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DeRes: Decoupling Residual Stability and Adaptivity for Scalable CTR Prediction
DeRes decouples residual stability and adaptivity via identity and block-attention paths with SiLU pointwise attention, delivering up to 0.32% AUC gains and steeper scaling laws on industrial and public CTR datasets.
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UniFormer: Efficient and Unified Model-Centric Scaling for Industrial Recommendation
UniFormer introduces a unified model-centric scaling approach for recommender systems via feature-space and task-space modules, semantic tokenization, and multi-sequence attention, with reported gains in production A/B tests at Kuaishou.