Learned representations are central to modern machine learning and are commonly evaluated through predictive performance, robustness, uncertainty estimation, or generalization. However, a learned representation may remain operationally successful while progressively failing to organize persistent residual structures that are not fully captured by conventional evaluation metrics. This article introduces VER, the Vigilant Evaluator of Representations, a conceptual framework for monitoring representational adequacy in learned representations. VER does not propose a new learning algorithm, loss function, or model architecture. Instead, it formalizes a diagnostic process through which persistent residual structures may be identified, analyzed, and interpreted as potential indicators of explanatory insufficiency. The framework distinguishes representational inadequacy from ordinary prediction error, uncertainty, noise, and distribution shift. It introduces a monitoring sequence based on representation identification, explanatory-domain delimitation, residual-structure detection, explanatory-resistance evaluation, and vigilance signaling. VER is intended as a contribution to representation diagnostics in machine learning. Its objective is not to replace existing evaluation methods but to complement them by treating representational adequacy as an explicit object of inquiry. A path toward empirical evaluation through representational-vigilance benchmarks is also outlined.

翻译：学习表示是现代机器学习的核心，通常通过预测性能、鲁棒性、不确定性估计或泛化能力进行评估。然而，学习表示可能在操作上保持成功的同时，渐进性地未能组织那些未被传统评估指标充分捕获的持久残余结构。本文提出VER（表征警觉性评估器），这是一个用于监控学习表示表征充分性的概念框架。VER不提出新的学习算法、损失函数或模型架构，而是形式化一种诊断过程，通过该过程可识别、分析持久残余结构，并将其解释为解释不充分性的潜在指标。该框架将表征不充分性与普通预测误差、不确定性、噪声和分布偏移区分开来，并引入基于表示识别、解释域界定、残余结构检测、解释抵抗性评估和警觉性信号传递的监控序列。VER旨在成为机器学习中表示诊断的贡献，其目标并非取代现有评估方法，而是通过将表征充分性作为显式探究对象来对其加以补充。本文还概述了通过表征警觉性基准迈向实证评估的路径。