AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Evaluating models on human bilingual and second language reading-time prediction and grammaticality judgement tasks, it is found that staged and temporally structured curricula consistently improve alignment with language learner reading time compared to balanced bilingual training.
BuzzASR is introduced, a collection of language-specialized fine-tuned Whisper models adapted for automatic speech recognition (ASR) in 102 languages, which achieve state-of-the-art CER among open-source systems on 27 of 102 languages on the combined FLEURS and Common Voice test set.
The results show that several widely used normalized metrics introduce crosslinguistic biases rooted in tokenization, encoding, and orthographic differences, and sentence-level negative log-likelihood computed over semantically equivalent sequences provides more meaningful and consistent crosslingual comparisons.
It is confirmed that cross-lingual alignment can emerge from the structure of language and the information it carries rather than from joint training, and this points to practical future directions including model stitching, merging, and modular multilingual systems built from monolingual components.
This work quantifies cross-lingual skill inconsistency orthogonally from knowledge and general benchmark performance via multilingual self-play, and shows that skill discrepancies are a measurable major roadblock in the development of truly multilingual models.
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