Learning to Rotate: Temporal and Semantic Rotary Encoding for Sequential Modeling
By: Hailing Cheng, Daqi Sun, Xinyu Lu
Published: 2026-04-28
View on arXiv →#cs.AI#Dynamic Pricing#Interpretability#Machine Learning#E-commerce#Attribute-Level Modeling#Cold Start ProblemRetailE-commerceLogisticsTravel & Hospitality
Abstract
This paper introduces a novel temporal and semantic rotary encoding method designed to improve sequential modeling, offering significant advancements for tasks involving complex time-series data and dynamic systems. Its potential applications range from enhanced natural language processing to more robust robotic control and predictive analytics in various industries.