Mechanism diagram · Hybrid / Mixer
How it works
Inputs pass through an embedding then through a router that learns to softly assign weight across multiple KAN expert subnetworks.
Each KAN expert uses spline-based learnable univariate transforms instead of fixed activations.
Expert outputs are weighted and aggregated for the forecast head.
Pros and cons on this universe
Pros
- Strong on the major-long side (ETH +0.176, BNB +0.146 per-coin IC).
- Conditional computation — different inputs use different experts.
Cons / failure modes
- No dossier — added based on Nixtla curiosity arm; sparse internal literature.
- KAN family has thin published track record on financial perpetuals.
- Likely cluster-redundant given typical Nixtla mixer signal profile.