Traditional whole-slide image analysis relies on exhaustive patch-level processing that is computationally expensive at gigapixel scale. PathCTM formulates diagnosis as adaptive scale-space continuous reasoning, progressively moving from low-magnification global inspection to high-magnification local evidence gathering with dynamic scale switching, region pruning, and confidence-aware early stopping. It cuts required image patches and inference time by about 96% while maintaining slide-level AUC.