Achieves depth-independent training memory bounded to approximately twice the inference footprint.
March 20, 2026
Original Paper
Adaptive Domain Models: Bayesian Evolution, Warm Rotation, and Principled Training for Geometric and Neuromorphic AI
arXiv · 2603.18104
The Takeaway
This challenges the fundamental assumption that training must be significantly more memory-intensive than inference. By utilizing posit arithmetic and a unique dimensional type system, it enables training on hardware typically reserved for inference-only tasks, drastically lowering the barrier for edge and domain-specific training.
From the abstract
Prevailing AI training infrastructure assumes reverse-mode automatic differentiation over IEEE-754 arithmetic. The memory overhead of training relative to inference, optimizer complexity, and structural degradation of geometric properties through training are consequences of this arithmetic substrate. This paper develops an alternative training architecture grounded in three prior results: the Dimensional Type System and Deterministic Memory Management framework [6], which establishes stack-elig