IAT compresses each historical interaction instance into a unified embedding token via temporal-order or user-order schemes, allowing standard sequence models to learn long-range preferences with better performance and transferability.
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2026 9roles
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UG-Separation framework disentangles user-side and item-side flows in TokenMixer dense-interaction models to enable reusable user computations, cutting inference latency up to 20% in ByteDance production scenarios.
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.
RankUp raises effective rank of representations in deep MetaFormer recommenders via randomized splitting and multi-embeddings, delivering 2-5% GMV gains in production deployments at Weixin.
A verification-aware agent harness that proposes and semantically checks recommender architecture changes reported the best effective pass rates in Tencent's tests and positive GMV gains (+1.25% to +2.02%) in a production A/B test.
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.
Token Factory maps dense, sparse, and sequence features into learned soft tokens, cutting LRM prompt length by ~70% while keeping ranking quality on par and improving retrieval of fresh videos.
Taiji presents a LLM-as-Enhancer system with reverse-engineered CoT data generation and Pareto Optimal Policy Optimization (POPO) to trade off semantic and ID rewards, deployed at Kuaishou serving 400M daily users.
Rec-Distill is an industrial distillation pipeline that transfers substantial performance from large-scale recommendation models to efficient students, reporting over 60% transferability and measurable business gains.
citing papers explorer
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IAT: Instance-As-Token Compression for Historical User Sequence Modeling in Industrial Recommender Systems
IAT compresses each historical interaction instance into a unified embedding token via temporal-order or user-order schemes, allowing standard sequence models to learn long-range preferences with better performance and transferability.
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Compute Only Once: UG-Separation for Efficient Large Recommendation Models
UG-Separation framework disentangles user-side and item-side flows in TokenMixer dense-interaction models to enable reusable user computations, cutting inference latency up to 20% in ByteDance production scenarios.
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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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RankUp: Towards High-rank Representations for Large Scale Advertising Recommender Systems
RankUp raises effective rank of representations in deep MetaFormer recommenders via randomized splitting and multi-embeddings, delivering 2-5% GMV gains in production deployments at Weixin.
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NOVA: A Verification-Aware Agent Harness for Architecture Evolution in Industrial Recommender Systems
A verification-aware agent harness that proposes and semantically checks recommender architecture changes reported the best effective pass rates in Tencent's tests and positive GMV gains (+1.25% to +2.02%) in a production A/B test.
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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.
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Token Factory: Efficiently Integrating Diverse Signals into Large Recommendation Models
Token Factory maps dense, sparse, and sequence features into learned soft tokens, cutting LRM prompt length by ~70% while keeping ranking quality on par and improving retrieval of fresh videos.
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Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation
Taiji presents a LLM-as-Enhancer system with reverse-engineered CoT data generation and Pareto Optimal Policy Optimization (POPO) to trade off semantic and ID rewards, deployed at Kuaishou serving 400M daily users.
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Rec-Distill: An Industrial Distillation Pipeline for Large-Scale Recommendation Models
Rec-Distill is an industrial distillation pipeline that transfers substantial performance from large-scale recommendation models to efficient students, reporting over 60% transferability and measurable business gains.