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, E...
| Authors | Jacques Raynal, Pierre Slangen, Elsa Raynal et al. |
|---|---|
| Primary category | cs.LG |
| Published | 11 June 2026 |
| Last updated at source | 21 September 2026 |
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