Mechanism diagram · MLP / Linear
How it works
KAN replaces fixed activation functions with learnable univariate transforms — typically B-splines.
Each edge in the network carries its own learnable spline; the composition of these splines represents the multivariate function.
On forecasting tasks, KAN layers map a flattened lookback to the forecast horizon via stacked spline edges.
Pros and cons on this universe
Pros
- Strong BNB long (+0.268) and ETH long (+0.262) per-coin IC.
- Theoretically grounded in the Kolmogorov–Arnold representation theorem.
- Rank #18 IC overall.
Cons / failure modes
- Pass A vs walk-forward divergence (negative Pass A IC −0.043 vs walk-forward IC −0.009) — flagged regime-fragile.
- Cluster overlap with KAN-derived mixer (RMoK).
- STRAT-13 deployment was gated by regime fragility.