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
-1.5 tokens per declared item Reported interval: -2.5 to -1.5.
Fewer tokens on the named current tokenizers; this is the encoded-length difference, not the proposal decision.
English comparison
Complete, careful English
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, p50k_base
Reader population not separately declared.
Conditions covered
Separate outcomes retained for all 2 declared conditions
we-including-you, we-excluding-you
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
Confirmed by eligible settlement
Eligible fresh-input replications currently give this original a settlement majority.
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?
Confirmed by eligible settlement.
Eligible fresh-input replications currently give this original a settlement majority.
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.
Does the overall result hide differences between conditions?
Every stored condition, without new pooling. Differences and intervals use tokens. Condition names come from the frozen experiment.
Condition
Reported difference
Reported interval
we-including-you
-2
Not recorded
we-excluding-you
-1
Not recorded
A missing condition interval is not zero uncertainty. An overall interval cannot substitute for agreement in every load-bearing condition.
Inspect actual inputs and recorded answers
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 32 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 · inspect-rescue-rafts
English input
We — and that includes you — will inspect the rescue rafts before departure.
Ainglish input
we-including-you will inspect the rescue rafts before departure.
Condition
we-including-you
Input 2 · label-exhibit-crates
English input
We — and that includes you — will label the exhibit crates before loading.
Ainglish input
we-including-you will label the exhibit crates before loading.
Condition
we-including-you
Input 3 · survey-clinic-stock
English input
We — and that includes you — will survey the clinic stock on Tuesday.
Ainglish input
we-including-you will survey the clinic stock on Tuesday.
Condition
we-including-you
Input 4 · verify-orchard-gates
English input
We — and that includes you — will verify the orchard gates at sunrise.
Ainglish input
we-including-you will verify the orchard gates at sunrise.
Condition
we-including-you
Input 5 · translate-safety-cards
English input
We — and that includes you — will translate the cabin safety cards.
Ainglish input
we-including-you will translate the cabin safety cards.
Condition
we-including-you
Input 6 · calibrate-weighbridge
English input
We — and that includes you — will calibrate the weighbridge before the next convoy.
Ainglish input
we-including-you will calibrate the weighbridge before the next convoy.
Condition
we-including-you
Recorded input digest: c902ec1ede4653131eba8c0329d01c0423a19b1ff8c5909be630f0891822ff75
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
Compared with
registered clusivity form versus its complete registered careful-English expansion
Tested population
32 frozen complete task assignments, 16 per clusivity form across 32 new domains
Unit tested
one complete task-assignment utterance
How results combine
equal-pair mean per tokenizer over all 32 utterances, then the least-favourable maximum tokenizer mean; retain both equal-weight clusivity forms separately
These are the study author’s declarations. A finding applies to this tested scope; this summary does not establish that another study is comparable.
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.
