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

The project proposes a radical departure from the status quo of deep learning (Backpropagation + GPUs + IEEE-754). By focusing on Bayesian Evolution and 'Warm Rotation,' it attempts to solve the 'structural degradation' of geometric properties during training—a known issue in manifold learning and geometric deep learning. With 0 stars and a 4-day-old presence, the project is currently a theoretical contribution or a nascent research repo. Its defensibility is low because it lacks code traction, community, or a functional library. However, the frontier risk is low because major labs (OpenAI, Google) are currently hyper-optimized for the transformer/backprop paradigm and are unlikely to pivot to niche neuromorphic training regimes until they show significant scaling advantages. The 'Warm Rotation' concept suggests a mechanism for maintaining unitary or orthogonal constraints in weights without expensive re-parameterization. Competitors include Geoffrey Hinton's Forward-Forward algorithm and DeepMind's synthetic gradients, both of which also attempt to move beyond standard backprop. The project's success depends on the validity of its 'Dimensional Type System,' which aims to provide memory safety for non-standard hardware.