Detecting Explanatory Insufficiency in Learned Representations: A Framework for Representational Vigilance
arXiv:2606.13172
The paper proposes VER, a conceptual framework for monitoring learned representations to detect unexplained residual structures that standard performance metrics miss, offering a diagnostic process for assessing representational adequacy.
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.
22 pages, 2 figures, no tables, 16 references. Conceptual and methodological framework for monitoring representational adequacy and detecting explanatory insufficiency in learned representations