Adaptive Domain Models: Bayesian Evolution, Warm Rotation, and Principled Training for Geometric and Neuromorphic AI

The paper presents Adaptive Domain Models (ADM), an alternative training architecture that mitigates the limitations of traditional reverse-mode automatic differentiation by employing a combination of prior frameworks, resulting in approximately twice the inference memory footprint for training. Key innovations include Bayesian distillation for domain-specific training, which addresses data scarcity, and warm rotation for seamless model updates without service interruption. This approach allows for the development of smaller, more precise AI systems that maintain correctness and adaptivity, enhancing the efficiency and applicability of models in both geometric and neuromorphic contexts.