Tag
Introduces CuBAS, an information-geometric framework for adaptive data selection in supervised classification that uses local curvature of the data manifold to identify informative samples, achieving improved accuracy across 30 benchmark datasets.
This post extends E8 lattice geometric activation injection to supervised LLM safety routing, using STE-snapped E8 policy heads. While achieving near-perfect routing on clean data, the approach catastrophically fails under adversarial stress, requiring a hybrid symbolic-geometric architecture with audited deterministic rules.