The way Chinese characters are built like a web might actually be a better way to understand how AI 'thinks' than English is.
March 25, 2026
Original Paper
Semantic Networks in Chinese Language as Model for Understanding
SSRN · 6191158
The Takeaway
Current AI research is dominated by English-speaking paradigms that treat language as a string of phonetic tokens. This paper argues that the compositional structure of Chinese—where meaning is embedded in visual components—better reflects the internal architecture of neural networks.
From the abstract
This paper proposes that the compositional logic of Chinese character systems-where meaning is structurally embedded in visual components and characters form natural semantic networksprovides a more accurate conceptual model for how artificial neural networks process meaning than the sequential, token-based frameworks inherited from alphabetic language traditions. Current AI interpretability research overwhelmingly operates within conceptual paradigms shaped by English and other alphabetic langu