A Typed Latent-State Architecture for Experience-Conditioned Structural Revision

We introduce a typed structural latent-state architecture in which experience revises the executable structures governing an agent's subsequent prediction, action, and learning. Its learnable state is a discrete structural world model: concepts are typed Metadata clusters whose generation structures, scopes, and relations participate directly in identity and computation. We implement finite slices in software agents in simulated worlds. In early studies, matched version-space and complete-context cache controls reproduce the tested prediction and reuse behaviors. In later studies, experienced outcomes change method selection and active interfaces, while failure attribution produces structures that persist across processes and guide later candidate generation. Across twelve new experience streams, an interface-revision policy improves predictive accuracy over the frozen predecessor in ten and ties in two, but yields lower cost-adjusted returns in all twelve under the tested tariffs. Revision does not by itself ensure model adequacy: a connected attribution–revision–reuse loop can still generalize incorrectly when the true relation lies outside its available hypothesis family. Matched interventions on what later computation consumes establish executable experience-conditioned revision of an agent's computational state.