SID-MLP distills autoregressive generative recommenders into efficient position-specific MLP heads for Semantic ID tasks, achieving 8.74x faster inference with matching accuracy.
Autoregressive image generation using residual quantization
5 Pith papers cite this work. Polarity classification is still indexing.
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2026 5representative citing papers
SAPO computes per-reasoning-step group-relative advantages in RL to improve credit assignment for structured generation of semantic identifiers in recommendation systems.
InsightTok improves text and face fidelity in discrete image tokenization via content-aware perceptual losses, with gains transferring to autoregressive generation.
Agentic Recommender Systems turn static recommendation pipelines into self-evolving collections of agents using reinforcement learning and LLM-driven architecture generation.
AutoModel uses three core agents (AutoTrain, AutoFeature, AutoPerf) connected by a shared coordination layer to automate model design, feature evolution, performance management, and paper-driven reproduction in large-scale recommender systems.
citing papers explorer
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MLPs are Efficient Distilled Generative Recommenders
SID-MLP distills autoregressive generative recommenders into efficient position-specific MLP heads for Semantic ID tasks, achieving 8.74x faster inference with matching accuracy.
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SAPO: Step-Aligned Policy Optimization for Reasoning-Based Generative Recommendation
SAPO computes per-reasoning-step group-relative advantages in RL to improve credit assignment for structured generation of semantic identifiers in recommendation systems.
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InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation
InsightTok improves text and face fidelity in discrete image tokenization via content-aware perceptual losses, with gains transferring to autoregressive generation.
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Rethinking Recommendation Paradigms: From Pipelines to Agentic Recommender Systems
Agentic Recommender Systems turn static recommendation pipelines into self-evolving collections of agents using reinforcement learning and LLM-driven architecture generation.
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AgenticRS-Architecture: System Design for Agentic Recommender Systems
AutoModel uses three core agents (AutoTrain, AutoFeature, AutoPerf) connected by a shared coordination layer to automate model design, feature evolution, performance management, and paper-driven reproduction in large-scale recommender systems.