Feasibility Analysis of Transformer Algorithm Improvement — Position Encoding Reconstruction Based on the Survey Line Spiral Envelope Interface

The Transformer algorithm has achieved remarkable success in natural language processing and multimodal learning, yet its position encoding mechanisms — whether fixed sinusoidal encoding, learnable position encoding, or rotary position encoding (RoPE) — all lack a unified geometric-physical foundation. Based on World Quantum Theory and the Survey Line Spiral framework, this paper proposes a new position encoding scheme: reinterpreting the embedding process of tokens within a slice as the convergence process of a frequency-layered vortex on an envelope surface. We argue that the 512 token points in each frequency layer are not randomly distributed on a plane but are naturally arranged on the equiangular curve of the Survey Line Spiral; the essence of token encoding is the unique phase-locked value formed by the convergence of the 512 points in that layer on the envelope surface. This paper proposes introducing the 90-degree step equiangular property of the Survey Line Spiral into position encoding as a replacement for existing sinusoidal/rotary encoding, and provides theoretical analysis and engineering feasibility recommendations. Keywords: Transformer, position encoding, Survey Line Spiral, envelope interface, logarithmic spiral, frequency-layered vortex, phase locking