#трансформеры
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Byte language models: long sequences as a resource, not a burden
Researchers in Shanghai show that a plain Transformer can learn directly from raw bytes without a tokenizer — and, as parameters scale, overtake subword models.
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A Transformer Scored 44% on ARC-AGI-1 for 67 Cents
Indian researcher Mithil Vakde trained a tiny model from scratch in an hour and a half on a single GPU and beat many large language models on accuracy — with no synthetic data and no recursion.
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The Canonical Basis: How to Rotate an LLM to Look Inside It
A researcher has proposed a reversible change of coordinates for the transformer that leaves its behavior untouched but makes every hidden axis individually measurable and controllable.