The paper claims that composing the Dimensional Type System, Program Hypergraph, and b-posit 2026 standard yields depth-independent training memory at ~2x inference, grade-preserving updates, Bayesian distillation for domain adaptation, and warm rotation for uninterrupted deployment.
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Adaptive Domain Models: Bayesian Evolution, Warm Rotation, and Principled Training for Geometric and Neuromorphic AI
The paper claims that composing the Dimensional Type System, Program Hypergraph, and b-posit 2026 standard yields depth-independent training memory at ~2x inference, grade-preserving updates, Bayesian distillation for domain adaptation, and warm rotation for uninterrupted deployment.