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

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arXiv:2606.13172v3 Announce Type: replace Abstract: Learned representations are central to modern machine learning and are typically evaluated through predictive performance, robustness, uncertainty estimation, and generalization. However, a learned representation may remain operationally successful while failing to organize persistent residual structures not fully captured by conventional evaluation metrics. This article introduces VER (Vigilant Evaluator of Representations), a conceptual framework for monitoring representational adequacy. VER does not propose a new learning algorithm, loss function, or model architecture. Instead, it defines a diagnostic process for identifying persistent residual structures and assessing whether they may indicate explanatory insufficiency rather than uncertainty, noise, data limitation, local model error, or distribution shift. The framework comprises five operations: representation identification, explanatory-domain delimitation, residual-structure detection, explanatory-resistance evaluation, and vigilance signaling. VER complements performance evaluation, uncertainty estimation, out-of-distribution detection, and robustness analysis by making representational adequacy an explicit object of inquiry. A path toward empirical evaluation through representational-vigilance benchmarks is also outlined.

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