Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).
We introduce the Extender, a log-structured variant of the standard Transformer architecture. In a standard Transformer, each layer communicates with subsequent layers exclusively via the residual $\mathbf{h}$, a superposition channel. The Extender adds a concatenation channel $\mathbf{x}$: each layer $\ell$ emits both a residual update $δ_\ell$ which is added to $\mathbf{h}$, and a much smaller extension $ε_\ell$ which is appended to $\mathbf{x}$. While both the FFN and $\mathbf{q}$ see $\mathbf{h}$, the attention $\mathbf{kv}$ projections take only $\mathbf{x}$ as input. As a result, the fully extended $\mathbf{x}$ contains the complete input for the $\mathbf{kv}$ projections of all layers, reducing the persistent attention memory footprint from $2Ld_{model}$ to $\sum|ε_\ell|$. We find that with $|ε_\ell|=32$, the Extender matches Transformer accuracy on short-context (CORE) tasks at 199M-924M parameters, and exceeds Transformer accuracy on long-context (RULER) workloads, again at 924M parameters. For our 1664-wide, 924M model, the Extender's persistent attention memory footprint is $104\times$ smaller than MHA. The memory savings grow with model width.
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