Asymptotic Sufficiency Depth: Observation, Hidden Information, and Geometric Universality — Series Overview

This Series Overview introduces the organizing framework of Asymptotic Sufficiency Depth: at what asymptotic layer does information lost under observation first become distinguishable? It connects exact likelihood-ratio sufficiency, conditional hidden Kullback–Leibler divergence, weak-noise information loss, directional polarization, path-space coarse graining, and geometric localization across Volumes I–III. Volume I develops the finite-state foundations, Volume II extends the framework to path-space and continuous-time dynamics, and Volume III develops geometric universality, statistical-experiment connections, diagnostics, counterexamples, and open problems. Reproducibility software and validation materials are archived separately in Zenodo Software, version 1.0.0, DOI 10.5281/zenodo.22289467.