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Validation Loss by Model Size (bordered table)

Mon, 21 Sep 2026 15:02:00 +0900

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A scaling study compares models of different sizes trained on the same token budget. If the loss falls smoothly as capacity grows, the family is in a regime where parameters still buy accuracy, and the fall can be summarised as a power law in the parameter count rather than reported as a list of separate runs.

Attention Variants for Long Contexts (striped table)

Mon, 21 Sep 2026 07:32:00 +0900

Long-context attention is usually chosen on cost rather than on quality. What decides whether a variant is usable at a given sequence length is how the score cost and the per-token cache grow with \(T\), and both are easiest to compare side by side in one table rather than across several paragraphs.

Notation for Multi-Head Attention (academic table)

Mon, 21 Sep 2026 07:12:00 +0900

Multi-head attention reads better as shape bookkeeping than as prose: every symbol below is a tensor, and the shapes alone decide which products are legal, which is why papers about attention variants usually open with a notation table before showing a single result.