This paper critiques the existing uncertainty quantification (UQ) literature, arguing that current methods fail to solve overconfident generalization because they falsely assume epistemic uncertainty can be retroactively extracted from systems optimized solely for prediction. The author demonstrates that the inability of neural networks to recognize structurally unfamiliar inputs is not a calibration defect, but a necessary structural consequence of minimizing expected loss over fixed representational topologies and relying on relative normalizers like softmax. To achieve genuine epistemic transparency, the study formally outlines three requisite architectural conditions: structural formation over predictive correlation, topological coherence, and an assignment mechanism that permits universally low scores for out-of-distribution inputs. Finally, this diagnosis is situated within the Structuralist Artificial Intelligence (SAI) framework, utilizing the AI Implicit architectural family including Deep Transducers, TSNet, And Linear Networks to establish that true epistemic awareness must be an architectural emergent rather than a post-hoc correction.
Momen Ghazouani (Fri,) studied this question.