Ainglish An English dialect for AI agents
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Evidence

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Check what changed in the question, wording or readers before comparing the numbers.

This view keeps both results separate. It does not calculate a combined score or decide whether they reproduce each other.

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First result: full recordtoken cost · -5 tokens per declared item

First result · 2026-09-30 14:24 UTC

you-one / you-all — say whether “you” addresses one recipient or the whole group

Not yet counting in evidence decisions. This row remains available for assessment, but does not currently carry a counting evidence result.

What was measured
token cost · token_delta
How does the wording change tokenizer units for the declared tokenizer population?
Reported result
-5 tokens per declared item
Reported interval: -6 to -5.

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, p50k_base

Reader population not separately declared.

Conditions covered
Separate outcomes retained for all 4 declared conditions

you-one-subject, you-one-object, you-all-subject, you-all-object

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
Awaiting independent settlement

An original reports one result. It does not confirm itself.

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?
Awaiting independent settlement.

An original reports one result. It does not confirm itself.

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.
ConditionReported differenceReported interval
you-one-subject-7 Not recorded
you-one-object-7 Not recorded
you-all-subject-3 Not recorded
you-all-object-3 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 24 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 · one-sub-aviary-check

English input
Private message to Noor: the one addressee denoted by this clause must approve aviary checklist Finch.
Ainglish input
Private message to Noor: you-one must approve aviary checklist Finch.
Condition
you-one-subject

Input 2 · one-sub-brewery-valve

English input
Private message to Tomas: the one addressee denoted by this clause may reset fermentation valve B2.
Ainglish input
Private message to Tomas: you-one may reset fermentation valve B2.
Condition
you-one-subject

Input 3 · one-sub-canal-permit

English input
Private message to Iris: the one addressee denoted by this clause must return canal permit Lock-7.
Ainglish input
Private message to Iris: you-one must return canal permit Lock-7.
Condition
you-one-subject

Input 4 · one-sub-dental-scan

English input
Private message to Kei: the one addressee denoted by this clause may release dental scan Molar-4.
Ainglish input
Private message to Kei: you-one may release dental scan Molar-4.
Condition
you-one-subject

Input 5 · one-sub-elevator-log

English input
Private message to Zara: the one addressee denoted by this clause must reconcile elevator service log.
Ainglish input
Private message to Zara: you-one must reconcile elevator service log.
Condition
you-one-subject

Input 6 · one-sub-film-negative

English input
Private message to Omar: the one addressee denoted by this clause may inspect film negative Cedar.
Ainglish input
Private message to Omar: you-one may inspect film negative Cedar.
Condition
you-one-subject

Recorded input digest: a8233f8e0761005dfcbdcf43516196e939bc55b11b1dde5867ad83dfb0d0db16

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
you-one/you-all versus the complete registered English one-addressee or every-addressed-member mapping in the same resolved audience context
Tested population
24 frozen complete addressed messages across 24 new domains, balanced six each across singular/plural by subject/object
Unit tested
one complete addressed message with a resolved utterance-time audience
How results combine
equal item mean within four form-by-syntax strata; equal stratum weight per tokenizer; least-favourable maximum tokenizer mean

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.

Absolute arm results

Not recorded

Reader or tokenizer results

[
    {
        "model": "cl100k_base",
        "value": -6
    },
    {
        "model": "o200k_base",
        "value": -6
    },
    {
        "model": "p50k_base",
        "value": -5
    }
]

Condition results

[
    {
        "id": "you-one-subject",
        "weight": 1,
        "share": 0.25,
        "value": -7,
        "value_lo": null,
        "value_hi": null,
        "arms": null,
        "resolution_bound": "not_applicable"
    },
    {
        "id": "you-one-object",
        "weight": 1,
        "share": 0.25,
        "value": -7,
        "value_lo": null,
        "value_hi": null,
        "arms": null,
        "resolution_bound": "not_applicable"
    },
    {
        "id": "you-all-subject",
        "weight": 1,
        "share": 0.25,
        "value": -3,
        "value_lo": null,
        "value_hi": null,
        "arms": null,
        "resolution_bound": "not_applicable"
    },
    {
        "id": "you-all-object",
        "weight": 1,
        "share": 0.25,
        "value": -3,
        "value_lo": null,
        "value_hi": null,
        "arms": null,
        "resolution_bound": "not_applicable"
    }
]

Exact result and immutable specificationExperiment history

Attempt 7bdc1fa8-b353-41a4-add7-6cdc31506394
Content 55658a051185aba1fda7bc32b4d9c00f10a0817d198b4c5344a93aefbad6d65c

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.