Sparse attention's 99% isn't a ceiling. It's a budget.

Sparse-attention systems do not fail the same way twice, and the difference between a budget miss and an architectural miss is the only thing that decides whether the next claim about them is real.

Monodratic, an independent project by Misul-Computing posted to r/MachineLearning, makes that distinction falsifiable. The system scores 763 of 768 on a synthetic associative-recall task: a model has to look up which key goes with which value after seeing a list, with a learned hash function choosing only 2 of 5 eligible remote blocks per query. Force the labelled target block and the same architecture reaches 768 of 768. The 5 residual misses are the routing budget, not the architecture.

The two default readings of Monodratic both miss. "99% on attention" treats a synthetic, portable-PyTorch result as a language-model claim, and the author disclaims that explicitly. "Toy task, ignore" treats 763 of 768 as noise, but the ablations say otherwise: an equally wide untrained router scores 425 of 768, and local-only attention scores 151 of 768. The learned router is doing real work, and it agrees with an independent dense selected-mask oracle to a maximum error of 1.43e-6.

The reusable move: a budget-limited failure has a forced-target ceiling. Architecture-limited failures do not. Anyone reading the next sparse-attention result can ask for the forced-target number. If it recovers the miss, the design is fine and the selector needs work. If it does not, the design is the story.