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

Abstract

Learned representations are central to modern machine learning, but predictive performance, robustness, uncertainty estimation, and generalization do not by themselves establish representational adequacy. A model may remain operationally successful while preserving structured residuals that indicate explanatory insufficiency. We introduce VER (Vigilant Evaluator of Representations), a conceptual and methodological framework for monitoring learned representations and detecting when their limits become scientifically relevant. VER does not propose a new learning algorithm, loss function, or model architecture. It defines a diagnostic process that identifies persistent residual structure and evaluates whether it is better explained by uncertainty, noise, data limitations, local model error, distribution shift, or a limitation of the active representation. The framework comprises five operations: representation identification, explanatory-domain delimitation, residual-structure detection, explanatory-resistance evaluation, and vigilance signaling. VER complements conventional performance evaluation by making representational adequacy an explicit object of inquiry. It provides an operational bridge between representation learning and the diagnosis of explanatory insufficiency described by the Bootstrap Theory of Representational Emergence (TBER). The long-term objective is to support systems capable not only of learning representations, but also of recognizing when those representations no longer provide an adequate basis for explanation, generalization, or further reasoning.

14 pages, 2 figures, no tables, 16 references. Version 3. Conceptual and methodological framework for monitoring representational adequacy and detecting explanatory insufficiency in learned representations. Introduces a five-operation representational-vigilance process

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