Text encoding changes byte-level language model accuracy by language
Aether uses script-specific encodings with UTF-8 fallback; Chinese and Korean gain, while Georgian and Tamil do not.
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Aether uses script-specific encodings with UTF-8 fallback; Chinese and Korean gain, while Georgian and Tamil do not.

Thirteen languages are live now and 14 more are coming, for 31 languages after rollout.
Belebele recall@1 rises 14.4 to 26.0 points, to 75.2%–77.7%, while English and Russian recall fall.
It beats conventional on-policy self-distillation on math reasoning in ten languages, especially lower-resource ones.
Language-subspace interventions improve multilingual accuracy by only 1.60 points but sacrifice response-language fidelity in Qwen2.5-7B.
The post title describes promptable, adaptable multilingual transcription; the feed had no further details.
On 190 harmful behaviours, only an exploratory Mistral Small 4 Spanish swap stayed significant after correction.
After copy-paste strips invisible characters, detection needs about 2 English or Persian code points but 93 Lao.
SSU-Wanda+TIES best preserved source languages in single-target expansion; recursive multi-target updates were highly order-sensitive.
On about 200 labels, logistic regression led every field, while the most accurate system ignored 18% of emails needing action.

A single model replaces translate-then-detect and is about 20–800 times faster per item and an order of magnitude cheaper.
Code and weights are public; adaptive allocation beat fixed-rate coding on reconstruction and intelligibility at low event rates.
Across 28 model configurations, even frontier systems reach at most 22% accuracy in some communities.
The family keeps 30 languages and 16 Chinese dialects, with file, streaming, and interactive variants.
Weighting math answers by decoding speed beat majority voting by up to 4.2 points at no extra cost.
The inference-time method was tested on 18 languages and English bitext mining against more than 200 languages.
Prefilling only the gold answer's first token fixes a fifth to a half of multi-token English QA errors across 16 open models.
LLMs refuse more often in Indic languages when told they are under a safety test, including some benign requests.
Most smaller agent guards change safety verdicts when requests are translated, especially into rarer languages.
semCER weights OCR character edits by judged severity, matching readers better than standard CER.
LASEF finds English reasoning skeletons help smaller models slightly, but language choice is context-dependent.
Researchers propose dual minimal pairs and a mutual-information metric for more reliable LLM stereotype evaluation.
Acoustic ASV attackers generally beat content-oriented ones against multilingual voice anonymization.
HINTT's cascaded diarization and Qwen3-ASR pipeline beat a unified speech LLM on MLC-SLM Task 1.
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