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cs.IR2026
Bending the Scaling Law Curve in Large-Scale Recommendation Systems
Qin Ding, Kevin Course, Linjian Ma +19
Learning from user interaction history through sequential models has become a cornerstone of large-scale recommender systems. Recent advances in large language models have revealed…
cs.AI2026
PRISM: Parametrically Refactoring Inference for Speculative Sampling Draft Models
Xuliang Wang, Yuetao Chen, Maochan Zhen +5
Large Language Models (LLMs), constrained by their auto-regressive nature, suffer from slow decoding. Speculative decoding methods have emerged as a promising solution to accelerat…