Detecting Explanatory Insufficiency in Learned Representations: A Framework for Representational Vigilance

Learned representations are commonly evaluated through predictive performance, calibration, robustness, uncertainty estimation, and behavior under distribution shift. Yet these criteria do not determine whether the representation itself remains sufficient for organizing observations relevant to an explanatory task. We develop VER (Vigilance Explicative et Representationnelle; explanatory and representational vigilance) around a narrower question: can a system detect when its current representation becomes explanatorily insufficient without confusing insufficiency with ordinary error, missing data, uncertainty, or distribution shift? The current VER framework follows seven stages: TRACE, EVIDENCE, INSUFFICIENCY, REGIME HYPOTHESES, TRANSITION ASSESSMENT, VIGILANCE, and PROBE. It treats structured residuals as signals rather than verdicts, distinguishes data insufficiency from representation insufficiency, preserves multiple hypotheses under non-identifiability, allows a legitimate NON-DETERMINED outcome, and requires a discriminating Probe before stronger conclusions are drawn. It also separates variation, drift, adaptation, and representational transition, and introduces Present Enrichment before attributing explanatory value to history. Two paired thought experiments and a falsifiable benchmark are proposed. VER is not claimed to be empirically validated here; its contribution is an explicit architecture designed for auditability and for selecting observations that discriminate among competing explanations.