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TWIG: A Time-Causal Wavelet Operator for Autoregressive Forecasting on Irregular Graphs

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

We introduce TWIG (Time-Causal Wavelet Operator for Irregular Graphs), a graph-native neural operator for autoregressive surrogate modeling on static irregular graphs. TWIG transforms each node history into causal multiscale temporal features that separate recent variation from progressively slower memory components, then propagates these features through graph-wavelet operator blocks with gated pointwise channel mixing. The architecture is causal by construction and designed for closed-loop forecasting, where predictions are recursively reused as future inputs. We evaluate TWIG on three irreg

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