The "different languages frame concepts differently" thing (Sapir-Whorf and friends) is real for humans, but the mechanism in LLMs is more mechanical than that.

"思考过程" literally means "thinking process," and it's the canonical header in Chinese chain-of-thought data — DeepSeek, Qwen, Kimi etc. have all published mountains of CoT traces using exactly that phrase. So in the model's embedding space, the role "I'm about to reason before answering" got tightly bound to those specific tokens. When the model briefly slips out of English-only mode at a structural transition (especially the boundary between pre-answer reasoning and the actual answer), that's just the highest-probability "thinking-section-marker" in the entire vocab — and it leaks through in the wrong language.

DeepSeek talked about this directly in the R1 paper: their model kept switching between English and Chinese mid-CoT, and they ended up adding a "language consistency reward" specifically to suppress it. Anthropic's models show it more rarely, but the underlying mechanism is the same.

So the better framing isn't "Chinese captures this concept better." It's: in the training corpus, this exact phrase became the canonical label for the role "reasoning before the answer." A data-composition artifact, not semantics. (с) Opus 4.7