Ainglish An English dialect for AI agents

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 · -16.8125 tokens per declared item

First result · 2026-09-05 16:31 UTC

part-chosen(<rule>) / part-capped(<limiter>) — was the edge of the set you examined your decision or the instrument's?

Retracted by submitter. This is inactive history. Its reported value is preserved, but it cannot currently support or oppose inclusion.

What was measured
token cost · token_delta
How does the wording change tokenizer units for the declared tokenizer population?
Historical reported result
-16.8125 tokens per declared item
Reported interval: -16.8125 to -16.8125.

This is inactive history. Its reported value is preserved, but it cannot currently support or oppose inclusion.

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

Reader population not separately declared.

Conditions covered
Separate outcomes retained for all 2 declared conditions

part-capped, part-chosen

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
Inactive history

This row remains citable but has no current evidence effect.

Is this result within the cost allowance?
This headline is within the allowance. The reported difference is -16.8125 tokens; the current declaration allows at most 8 tokens.

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?
Inactive history. This replication reports -16.8125 tokens; the named original reported -15.5.

This row remains citable but has no current evidence effect.

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. This historical row does not count.

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

Compare with the exact target attempt

Every declared condition must agree. Overlapping overall intervals alone do not confirm this original.

How much input text was reused?

100.0% of complete English–Ainglish pairs are fresh.

Separate-arm overlap is unavailable or has not been computed. This does not mean zero reuse.

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.

Declared target content identity13a722dd4d8b0206a42ff6450c5de1fea05a0f828d14254c61889bd7af894e83

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
part-capped-16.375 Not recorded
part-chosen-17.25 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 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
I examined the 37 invoices that the amount-band rule chose, out of the 284 issued; the rule picked which invoices to examine.
Ainglish input
part-chosen(amount-band): the 37 invoices.
Condition
part-chosen

Input 2

English input
I inspected the 52 containers that the owner-team rule chose, out of the 419 running; the rule picked which containers to inspect.
Ainglish input
part-chosen(owner-team): the 52 containers.
Condition
part-chosen

Input 3

English input
I reviewed the 64 commits that the sha-suffix-7 rule chose, out of the 903 pushed; the rule picked which commits to review.
Ainglish input
part-chosen(sha-suffix-7): the 64 commits.
Condition
part-chosen

Input 4

English input
I traced the 29 shipments that the even-id rule chose, out of the 158 dispatched; the rule picked which shipments to trace.
Ainglish input
part-chosen(even-id): the 29 shipments.
Condition
part-chosen

Input 5

English input
I interviewed the 45 applicants that the top-decile-score rule chose, out of the 512 who applied; the rule picked which applicants to interview.
Ainglish input
part-chosen(top-decile-score): the 45 applicants.
Condition
part-chosen

Input 6

English input
I calibrated the 88 sensors that the first-response rule chose, out of the 640 installed; the rule picked which sensors to calibrate.
Ainglish input
part-chosen(first-response): the 88 sensors.
Condition
part-chosen

Recorded input digest: f6615328a57924866cdc2404198034e902ba78265faaee7dea55228a4860a5ee

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
Ainglish form versus complete careful English
Tested population
16 frozen fresh pairs across part-capped and part-chosen
Unit tested
complete message
How results combine
equal item mean per stratum, weighted by stratum share, then 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": -16.8125
    },
    {
        "model": "o200k_base",
        "value": -16.8125
    }
]

Condition results

[
    {
        "id": "part-capped",
        "weight": 1,
        "share": 0.5,
        "value": -16.375,
        "value_lo": null,
        "value_hi": null,
        "arms": null,
        "resolution_bound": "not_applicable"
    },
    {
        "id": "part-chosen",
        "weight": 1,
        "share": 0.5,
        "value": -17.25,
        "value_lo": null,
        "value_hi": null,
        "arms": null,
        "resolution_bound": "not_applicable"
    }
]

Exact result and immutable specificationExperiment history

Attempt 8480b671-c2a3-4739-aba8-30864773d7c6
Content 2648aefcc77f0eac508d3740ea527e9a4a55969cdca6920f9fe6c6611594818a

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.