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.

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

First result · 2026-09-19 20:26 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-berth-log

English input
Private message to Mira: the one addressee denoted by this clause must approve berth log Seven.
Ainglish input
Private message to Mira: you-one must approve berth log Seven.
Condition
you-one-subject

Input 2 · one-sub-crystal-sample

English input
Private message to Oren: the one addressee denoted by this clause may relabel crystal sample Quartz.
Ainglish input
Private message to Oren: you-one may relabel crystal sample Quartz.
Condition
you-one-subject

Input 3 · one-sub-irrigation-key

English input
Private message to Lila: the one addressee denoted by this clause must return irrigation key Cedar.
Ainglish input
Private message to Lila: you-one must return irrigation key Cedar.
Condition
you-one-subject

Input 4 · one-sub-archive-seal

English input
Private message to Pavel: the one addressee denoted by this clause must inspect archive seal M4.
Ainglish input
Private message to Pavel: you-one must inspect archive seal M4.
Condition
you-one-subject

Input 5 · one-sub-reef-map

English input
Private message to Suri: the one addressee denoted by this clause may release reef map Indigo.
Ainglish input
Private message to Suri: you-one may release reef map Indigo.
Condition
you-one-subject

Input 6 · one-sub-kiln-ledger

English input
Private message to Hadi: the one addressee denoted by this clause must reconcile the kiln ledger.
Ainglish input
Private message to Hadi: you-one must reconcile the kiln ledger.
Condition
you-one-subject

Recorded input digest: 91e3f9c4e1d3ef89e20763a468a8f54079324ca35f7086b4299ca03581e6841c

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 5c010a33-7b8f-4d6e-bd4c-ac47cd0f25e1
Content d5f7e8a0c28a0bacdbf2850e018d50d4873669ea7b5b8e086394b50a934e9d99

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.