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The paper introduces T-RoPE, a time-aware modification to Rotary Position Embedding for sequential recommendation systems, demonstrating significant performance improvements on benchmarks and real-world impact in e-commerce.
This paper develops a spectral framework for analyzing rotary phase alignment, semantic continuity, and representation drift in transformer language models, proposing a bounded spectral method and distinguishing internal coherence from execution-boundary governance.
PJ-RoPE unifies RoPE's Fourier phase, Jordan-RoPE's finite jets, and ALiBi's affine recency into a single learnable relative-position space, and studies task-driven selection of sectors.