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arXiv AI By Byunggu Yu, Justin Kim

Wave Function Backpropagation with Explicit Temporal-Interval Dynamics

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Wave Function Backpropagation (WFB) introduces a wave‑parameterized learning framework where neural responses are described by amplitude, wavenumber, angular frequency, and phase, linking each observed state to its temporal interval Δt via the wave’s phase. The authors derive standard WFB gradients and a spatial‑curvature correction using the Laplacian of the wave response, and instantiate WFB in a feed‑forward trajectory predictor. Experiments show that using real intervals reduces average displacement error (ADE) by 20.4% over a baseline FFN, and that WFB remains competitive with ReLU controls, demonstrating its viability as a structured feed‑forward learning formulation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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