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The architecture ablation table reports that removing ContextNorm fails to converge, while additive fusion, auxiliary-loss budget control, and removing P-mask reduce the reported recall average relative to LoGo.
Optional independent architecture-ablation reconstruction
An experiment plan exists, but the current processing task has no matching execution target.
On the paper's 32k-checkpoint short-context recall aggregate, LoGo is reported as competitive with the other methods and scores 57.65 percentage points across six recall tasks.
Optional independent short-context recall reconstruction
An experiment plan exists, but the current processing task has no matching execution target.
In the controlled 1.5B comparison, LoGo reports the best language-modeling loss and Lambada perplexity among the listed full-attention and matched-budget static hybrid variants, with a higher commonsense-reasoning average than the full Transformer.
Optional independent matched-budget reconstruction
An experiment plan exists, but the current processing task has no matching execution target.
LoGo preserves the reported scaling behavior of full-attention Transformers from 200M through 3.3B parameters, matching or improving the baseline on the listed language-modeling and commonsense metrics.
Optional independent scaling-direction reconstruction
An experiment plan exists, but the current processing task has no matching execution target.
The paper reports that its query-sparse Triton kernel reaches a 1.99x forward-plus-backward speedup over the dense Triton baseline at 64k sequence length and a 0.5 attention budget.
This claim has no executable experiment plan yet.
LoGo reports higher average needle-style RULER recall than the three comparison paradigms at both the 32k and 128k context-extension stages, with averages of 83.0 and 65.4 percentage points.
Optional independent long-range recall reconstruction
An experiment plan exists, but the current processing task has no matching execution target.
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