We present a foundational formulation of the Bayesian Mirror Architecture (BMA), a self-referential generative framework in which sensory abstractions, meta-abstractions, and a self-latent interact through circular recursion.
The defining constraint is a closed update S_t <- H_{t-1}, where a hybrid event-self latent H_t binds self-representations to abstract world models and reinjects this coupling into the self-state.
Consciousness, in a restricted sense, is not an optimization objective nor a semantic label, but an architectural property of systems possessing this circular structure.
Because inference operates over posterior beliefs, BMA's intrinsic state space is a space of probability measures equipped with optimal-transport geometry.
Stability and coherence are formulated in the 2-Wasserstein metric on P_2, yielding coordinate-free notions of self-stability and hybrid coherence along belief trajectories.
We define a Causal Learning Regime (CLR) via bounds on Wasserstein belief drift together with an integration index capturing sustained coupling between self and world latents.
CLR diagnoses whether the environment contains learnable causal structure; it is not a marker of consciousness.
Global strict contractivity is not required: BMA may exhibit multiple coherent basins.
We define self-manifolds basin-wise as supports of invariant measures under local Wasserstein contractivity.
We identify Wasserstein epsilon-necks, transport bottlenecks where basins decouple, yielding a unique realized continuation in a vanishing-conductance limit.
We interpret this selection as choice: internally determined yet externally unpredictable at finite resolution.
Learning proceeds via variational free-energy minimization, with stability and agency emerging from what the environment affords to learn.