Composing dimensional types, program hypergraphs, and b-posits yields depth-independent training memory, grade-preserving geometric updates, Bayesian distillation, and certified warm model rotation for domain AI.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
CONDITIONAL 2representative citing papers
Program Hypergraphs lift binary semantic graphs to arbitrary-arity hyperedges so grade inference, k-simplex joins, and spatial co-location become first-class, jointly analyzable compilation facts.
citing papers explorer
-
Adaptive Domain Models: Bayesian Evolution, Warm Rotation, and Principled Training for Geometric and Neuromorphic AI
Composing dimensional types, program hypergraphs, and b-posits yields depth-independent training memory, grade-preserving geometric updates, Bayesian distillation, and certified warm model rotation for domain AI.
-
The Program Hypergraph: Multi-Way Relational Structure for Geometric Algebra, Spatial Compute, and Physics-Aware Compilation
Program Hypergraphs lift binary semantic graphs to arbitrary-arity hyperedges so grade inference, k-simplex joins, and spatial co-location become first-class, jointly analyzable compilation facts.