The Byte Latent Transformer (BLT) runs a large model once per patch and starts patches where a small model's next-byte entropy is high. Under tight budgets this rule skips computed values, such as the number after "= " in math. On BLT-1B at inference we trace the miss to the cue before a value: when the preceding bytes fix the next byte's type, the patcher's entropy is capped near log2 10 bits, close to a 10% budget's threshold. Entropy then starts a patch at 0% of values after a dollar sign and 19% after "= "; writing "is " instead of "= " raises the 19% to 66%. Version 1's two-byte lookup table, motivated by LZ novelty, acts as a learned detector of cues that tend to precede new values in its fitting text: on the solutions rewritten as prose it covers every value after a dollar sign and none after a plain space, and its margin over entropy falls from +42.1 to -8.9 points, partly because prose gives entropy more starts per value. In BLT-style models trained from scratch (1M-50M parameters, 128-byte context, 3 seeds per rule) and scored by teacher-forced exact match on answer spans, the table beats entropy at 50M by 36.8 points on computed results and 29.6 on final answers, which mostly copy the last computed result. A hand-written marker rule is not distinguishable from the table at 1M and 12M; its 50M control was not run. At 1M and 12M the table ran on a T4 GPU in fp16 and the baselines on an A40 in bf16; at 50M all ran on the A40. End to end, no 1M-50M model solves more than 2 of 100 GSM8K problems. A label-free probe of BLT-1B's patcher finds prose values, mostly after a dollar sign, but costs final answers. In six domains, no rule we tested beat entropy on exact match of whole words and numbers in more than one domain. The positive findings concern tight budgets on cue-formatted text. This version corrects version 1; the work is closed, with no venue intended.

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