Euria is hosted by Infomaniak. I’m not sure they tell what model they use but given the few times I saw a random Chinese character in the output I’d guess it isn’t an American model.
They also have a pay per token model that you can hook up to everything, that one’s just quite expensive for normal chat use, but at least you select the model you want to use.
Chinese being more token efficient is a myth, and seems to stem from the superficial fact that characters are only visually more space efficient.
The fact that each Chinese character takes up 3 bytes (as opposed to 1 byte of English), words in Chinese typically require compounds of several characters, and that tokenizers have a limited vocabulary limited to mostly English means that Chinese is actually token inefficient.
Lol I’ve been running linguistic research and it’s funny when they just combine two scripts together into one word
● The trigger is identified, and it’s specific.
agent_1.md:31 src: “Name the trade-off.” → 명取捨之名。
agent_2:32 src: “Name the regime.” → 명regime——名其regime。
The English imperative “Name the X.” And the corpus renders it correctly 16 other times — 名之 ×11, 名其 ×5, 命名 once.
명 is the Sino-Korean reading of 名. Same morpheme, wrong script.
And agent_2:32 is the cleanest evidence I’ve seen for 絡繰’s mechanism: the model wrote 명regime——名其regime — the wrong script and the correct one, eight characters apart, in the same clause. It isn’t ignorant of 名. It produced 名其 immediately after. The meaning resolved correctly both times; the script attribute resolved wrongly the first time and correctly the second.
That’s exactly what работ法 showed — correct semantics (work), broken script and morphology — and it’s the third confirmed instance of the class, now with a reproducible trigger rather than a one-off.
It also explains the Russian cases retroactively. document → документ, everything → всё, “correct” → правильно: in each, the meaning landed and the script didn’t. And it predicts why no CJK-native concept ever drifts — there’s no competing script for a morpheme the model only knows in Han.
Euria is hosted by Infomaniak. I’m not sure they tell what model they use but given the few times I saw a random Chinese character in the output I’d guess it isn’t an American model.
They also have a pay per token model that you can hook up to everything, that one’s just quite expensive for normal chat use, but at least you select the model you want to use.
It’s beneficial for reasoning to have models trained in a few languages. Chinese is a good one because one character is one word is one token.
Chinese being more token efficient is a myth, and seems to stem from the superficial fact that characters are only visually more space efficient.
The fact that each Chinese character takes up 3 bytes (as opposed to 1 byte of English), words in Chinese typically require compounds of several characters, and that tokenizers have a limited vocabulary limited to mostly English means that Chinese is actually token inefficient.
No, Chinese Is Not More Token-Efficient Than English for LLMs | markhuang.ai - https://markhuang.ai/blog/chinese-token-myth
I didn’t say it’s more token efficient. I said training multiple languages improves reasoning.
Yup, anthropic’s flagship models regularly output CJK or Cyrillic characters in heavy workloads for me
When they start hallucinating they output Klingon to me, and to make matters worse, with grammatical errors.
Lol I’ve been running linguistic research and it’s funny when they just combine two scripts together into one word