The field of artificial intelligence is moving towards a more integrated understanding of visual and linguistic information. Recent developments have shown that models can acquire an abstract and transferable geometric grammar, allowing them to generalize across different domains and tasks. This is evident in the ability of models to reconstruct ancient characters and understand complex syntax in low-resource languages. The use of structured linguistic cues and domain-adaptive pretraining has also led to significant improvements in reasoning performance and language translation. Noteworthy papers include: LingGym, which evaluates LLMs' capacity for meta-linguistic reasoning, and BIRD, which proposes an allograph-aware masked language modeling framework for bronze inscription restoration and dating. The paper on pictographic character reconstruction with Bezier curves also demonstrates superior performance in visual recognition challenges.