Figureure 1 · : Overview of the activation-based probing, intervention, and self-corFigure 1: Overview of the activation-based probing, intervention, and self-correction framework. The pipeline extracts VLM hidden states to train probes, analyze representational subspaces via SVCCA, causally steer activations along probe-derived count directions, and selectively trigger inference-time correction using an internal error detector.这张图概括 The Count Is There, but Misaligned 的整体方法流程。阅读时先看模块之间传递的训练信号,再看作者如何把目标拆成可优化的子问题。(d) Different color different shape Figure 12: Layer-wise SVCCA similarity scores for vari(d) Different color different shape Figure 12: Layer-wise SVCCA similarity scores for various Vision Language Models and Datasets. High scores indicate strong alignment between ground truth and output probes.这张可视化用来解释 The Count Is There, but Misaligned 学到的中间表征或对齐关系。重点看它是否支持正文里的机制判断。