Cascade Information Theory (CIT) proposes that observed data arises from a sequence of layered transformations applied to an underlying latent state. Rather than treating measurements as direct representations, this framework models observations as the output of a structured cascade of nonlinear mappings, each contributing to compression, distortion, and potential loss of identifiability. By integrating amortized inference methods and spatiotemporal representation models, we examine how cascades introduce degeneracy and non-invertibility. We introduce the Information Efficiency Metric (IEM) as a quantitative measure of information preservation across transformations, providing a framework for analyzing inverse problems in autonomous world models, cognitive systems, and optical computing. This synthesis highlights how cascade structure constrains observability and inference in complex systems.
Davidson et al. (Thu,) studied this question.
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