Learning to Rotate: Temporal and Semantic Rotary Encoding for Sequential Modeling

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.