Counts in current evidence decisions. This row currently contributes to evidence decisions. Its direction is separate from whether the proposal is ready for adoption.
What was measured
token cost · token_delta How does the wording change tokenizer units for the declared tokenizer population?
Reported result
-16.125 tokens per declared item Reported interval: -16.25 to -16.125.
Fewer tokens on the named current tokenizers; this is the encoded-length difference, not the proposal decision.
English comparison
English comparison not recorded as a structured label
Declared by the submitter; not a certification of equivalent information.
Tokenizer conditions
Literal encoding cost on the named current tokenizers; not comprehension.
Named instruments
cl100k_base, o200k_base
Reader population not separately declared.
Conditions covered
No condition-by-condition settlement contract recorded
An overall average can hide a weak condition. A condition list is not proof that every form or claim in the proposal was tested.
Settlement role
Agrees with the named original
This eligible row adds one agreement to the named original’s settlement tally.
Is this result within the cost allowance?
No numerical allowance is available in this proposal’s current structured evidence declaration. A prose prediction is not silently converted into a bound.
This compares Ainglish minus English with the current declaration, which may differ from the declaration when the result was filed. It checks the headline only: inspect any required per-form and per-tokenizer results too.
Has the original estimate been independently reproduced?
Agrees with the named original.
This replication reports -16.125 tokens; the named original reported -16.0625.
This eligible row adds one agreement to the named original’s settlement tally.
Reproduction asks whether fresh-input findings agree under the settlement rule. It does not ask whether either value satisfies the cost allowance.
Being within the cost allowance is not a completed prerequisite. Reproducing an original estimate is a separate check, not proof that the allowance is met. Current evidence status, settlement and every declared result still determine readiness.
How can one check pass while the other does not?
For example, an allowance of at most +3 tokens and an original estimate of +3 ask different questions. A replication of −0.5 is within that allowance but may disagree with the original. A replication of +3.25 may reproduce +3 within the settlement tolerance while exceeding the allowance.
These are illustrative numbers, not a new settlement rule. A cost saving is not a comprehension result, and a reproduced premium does not by itself mean a proposal should be adopted or rejected.
This result checks a named original, not every experiment on the proposal.Read its target original
100.0% of complete English–Ainglish pairs are fresh.
0 of 16 English inputs reuse text from either side of the original.
0 of 16 Ainglish inputs reuse text from either side of the original.
Exact text comparisons only; repeated occurrences count separately. Shared text can deserve scrutiny even when each complete pair is new. These arm counts are descriptive and do not change settlement eligibility.
The comparison label is the submitter’s declaration, not a semantic certification. Check that both versions preserve the information needed to answer the same question.
Numbers count only readable inputs attached to this receipt. They are not the experiment’s declared sample size or the number of reader calls.
Showing 1–6 of 16 readable, inline study items, in stored order—not a selection of successes. 0 control items are kept separate.
Each result has its own input pages. Positions across the two studies do not imply matched cases.
Input 1
English input
All deployment artifacts are signed for every in-scope case other than local-debug-symbols; this sentence makes no assertion about that explicitly excluded class.
Ainglish input
all deployment artifacts are signed except_l(local-debug-symbols).
Input 2
English input
Every audit event has a timestamp for every in-scope case other than synthetic-load-events; this sentence makes no assertion about that explicitly excluded class.
Ainglish input
every audit event has a timestamp except_l(synthetic-load-events).
Input 3
English input
All mirrors serve revision 84 for every in-scope case other than mirror-orchid-draining; this sentence makes no assertion about that explicitly excluded class.
Ainglish input
all mirrors serve revision 84 except_l(mirror-orchid-draining).
Input 4
English input
Every account completed recovery enrollment for every in-scope case other than service-break-glass-identities; this sentence makes no assertion about that explicitly excluded class.
Ainglish input
every account completed recovery enrollment except_l(service-break-glass-identities).
Input 5
English input
All data partitions passed validation for every in-scope case other than partition-quartz-rebuild; this sentence makes no assertion about that explicitly excluded class.
Ainglish input
all data partitions passed validation except_l(partition-quartz-rebuild).
Input 6
English input
Every webhook includes a replay nonce for every in-scope case other than legacy-partner-hooks; this sentence makes no assertion about that explicitly excluded class.
Ainglish input
every webhook includes a replay nonce except_l(legacy-partner-hooks).
Recorded input digest: 86e4aeed16742602c1436edf262ef6d7b8922581849e796098c8e0b45987d4f7
Prompts, reference material and other context can live elsewhere in the specification. Inputs and keys alone do not reconstruct every reader call or establish a fair comparison.
Declared population, method and retained outcomes
No structured study scope is declared here. Inspect the immutable manifest; do not infer a comparator or population from the headline.
Absolute arm results, reader-specific results and condition results below are retained values, not a newly pooled analysis. Accuracy arms use fractions from 0 to 1; their difference uses percentage points.
Different wording, readers, exposure or populations can legitimately produce different results. A visible reference is not training the model’s weights. Current models and tokenizers have learned English; future Ainglish-trained performance remains a research question.
