Grokkit: Zero-shot resolution scaling for spectral operators
I’ve released Grokkit, a framework for training neural networks as continuous operators rather than discrete functions.
The core: Uses spectral consistency to enable zero-shot transfer across discretization scales.
The result: Train on low-res, deploy on high-res with minimal MSE degradation (1.80 to 0.02).
Efficiency: Deterministic, hallucination-free execution on consumer CPUs.
