The "law of conformity," the finding that frequent words are semantically stable, has been treated as a broad regularity of language change. We show it does not hold for Korean. Using diachronic word embeddings trained on historical corpora spanning 500 years (15th–20th centuries), we find a robust positive correlation between frequency and semantic shift: high-frequency Korean words change more, not less. The pattern survives six robustness controls and is validated against an English replication. Partial correlation analysis reveals that the role of polysemy in mediating the frequency–change relationship is not fixed but depends on time resolution and corpus homogeneity. We connect the reversal to frequency-driven reductive processes, including grammaticalization, semantic bleaching, and domain shift, that are especially productive in Korean. The frequency–change relationship is not a fixed regularity but varies with language typology and analytical conditions.

Recent Large Vision-Language Models (LVLMs) have shown remarkable success in general semantic understanding. However, they still struggle with 3D spatial reasoning tasks, such as estimating metric distances or understanding precise relative positions. Previous works, like SpatialVLM, tried to address this by using synthesized spatial VQA dataset. However, they are fundamentally limited because their vision encoders are biased toward 2D patterns learned from image-text pairs. In this paper, we argue that this lack of 3D awareness is a critical bottleneck that cannot be solved by data scaling alone. To address this, we propose Pseudo Geometric Distillation (PseudoGD), a framework designed to help vision encoders internalize 3D geometric information using only standard 2D images. PseudoGD explicitly injects metric scale and structural context into the encoder through a Joint Training strategy. This approach optimizes geometric learning and spatial VQA tasks together, ensuring that the Large Language Model (LLM) aligns well with the improved visual features in real-time. Extensive experiments on the OmniSpatial benchmark demonstrate that PseudoGD achieves State-of-the-Art (SOTA) performance across various model architectures. Notably, significant improvements in Hypothetical Perspective Taking and Locate tasks prove that our model has effectively learned a physical sense of space.