Sparse autoencoders find human-readable features inside LLMs. I trained one per layer on a Japanese-English model and measured which features fire in both languages — then tested whether they actually matter, with controls. The correlation is beautiful. The causation is borderline. Both get reported.

Scope note, first paragraph as promised: one model (llm-jp-3-1.8B), hand-rolled TopK SAEs (the standard library doesn't support this architecture), 2M training tokens per layer, Tatoeba pairs for analysis, greedy decoding, pre-registered causal tests with random controls. No human labelled any feature — only the intervention numbers carry weight.

The question

Every Japanese AI company building bilingual systems needs to know: does the model represent 契約 and "contract" with one shared feature or two separate ones, and at which depth do languages merge? Most interpretability work studies English-heavy models.…