We introduce the precursor idea of Ambiance-Encoding Theory (AET, or Encoding-Modelling Theory), a formal framework distinguishing physical systems from their mathematical representations. Contemporary modeling conflates these layers: the *ambiance space* A (empirically accessible entities with minimal observational language) and the *encoding space* ENC (formal descriptions under chosen representation schemes). This conflation generates persistent validation ambiguities across physics, machine learning, and mathematics. The framework establishes at least three validation requirements, of which are dependent of another, (i) explicit ambiance specification independent of formalism, (ii) encoding justification from ambiance requirements rather than mathematical convenience, and (iii) systematic classification of structures as connected (ambiance-invariant) or unconnected (encoding artifacts). These conjectures or rather commitments create certain fixture of obligations for arbitrary theories, as well as models, notably within which can include of the neural network architectural construction and operatives of working models. The framework aimed to provide partially both apparatus for theory evaluation and constructive methodology for developing empirically anchored representations. More specifically, the original goal is aimed at filling a foundational gap in scientific and mathematical epistemology, or between operationalisation (working within actual instantiation) and descriptions (details unimplemented). Within such, we note that it is strictly concerned with the structure of modelling and representation, rather than proposing new learning algorithm, hence not a technical algorithmic contribution.
Khanh Gia Bui (Wed,) studied this question.