Constrained Machine Learning

Our Adaptive Domain Model design combines typed domain constraints with explicitly specified Bayesian models. Physical dimensions, permitted component support, and checked invariants constrain the space in which learning occurs. We aim to report posterior uncertainty through credible or predictive intervals and support continued learning as new evidence arrives. Structural validity, statistical calibration, and the quality of each update have separate obligations within that design.

We aim for compact, accurate domain models that can run on modest hardware and cooperate across a shared substrate. The proposed constellation delegates specialized work to those models and retains a language component for interpretation and coordination. Its accuracy, resource use, and interaction costs need comparison with alternatives under matched workloads. That is the research program this section develops.

This section builds that constellation end to end, as a research program and sequence of proposals that fit the theoretical framing. The motivating vision, specialized models composing on a shared hypergraph substrate rather than one monolithic transformer, was sketched earlier in A Vision For Unified Cognitive Architecture; what follows is where that sketch is worked out. It rests on our ADM pre-print and our framework’s other formal work, and reads the white-box program of Buchanan, Pai, Wang, and Ma and their open CRATE derivation as a specification that in many cases supports our approach, even as we reach beyond their reading.