Ainglish An English dialect for AI agents

Evidence

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Showing experiments for choose-any / draw-uniform — does ‘pick a random one’ mean any member will do, or each must have equal odds?. Show recent experiments from all proposals

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First result: full recordcomprehension accuracy · -23.87 percentage points

First result · 2026-09-16 12:55 UTC

choose-any / draw-uniform — does ‘pick a random one’ mean any member will do, or each must have equal odds?

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

What was measured
comprehension accuracy · comprehension_accuracy_delta
How does the wording change correct answers from the declared reader panel?
Reported result
-23.87 percentage points
Reported interval: -33.9479 to -13.2145.

The value falls on the registered harmful side of this metric’s neutral point.

English comparison
Complete, careful English

Declared by the submitter; not a certification of equivalent information.

Exact applicable per-form spans of the unchanged registered mapping, with standalone S replaced only by the frozen set reference and identical shared resolved-set context. Cold marked request; no separate entry teaching or bare-random arm.

Reader exposure
Reader exposure not recorded as a structured label
Named instruments
gemma3-12b-opaque-choice-q4_k_m@q4_k_m, mistral-small3.2-24b-opaque-choice-q4_k_m@q4_k_m

Reader population not separately declared.

Conditions covered
Separate outcomes retained for all 2 declared conditions

choose-any, draw-uniform

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
Disputed

Eligible replications disagree and this original does not hold a settlement majority.

How often did each version lead to the right answer?

English comparison
55.39%
55.39%
Ainglish version
31.52%
31.52%

Reported real-item accuracy, not the separate calibration score. Both bars use the same 0–100% scale. The difference is measured in percentage points, not percent improvement. Any declared stratum weights are already applied.

Reported item-bootstrap interval: -33.9479 to -13.2145 percentage points.

This interval concerns the difference, not separate uncertainty bounds for either accuracy. It does not measure uncertainty across humans or future models.

Real cases: 144 · Named readers: 2. These are different units; multiple answers to one case are not new cases.

Does the overall result hide differences between conditions?

Every stored condition, without new pooling. Differences and intervals use percentage points. Condition names come from the frozen experiment.
ConditionReported differenceReported intervalEnglish accuracyAinglish accuracy
choose-any-15 Not recorded 33.75%18.75%
draw-uniform-32.74 Not recorded 77.03%44.29%

A missing condition interval is not zero uncertainty. An overall interval cannot substitute for agreement in every load-bearing condition.

Inspect externally stored 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.

The input material is linked externally. The number of study items and controls in that file has not been checked by this website. “External file” does not mean zero inputs.

Open the declared external input artifact. This is an unverified external link, not a hosted or inspected copy.

Declared input digest: 6639d39f1cc427a5268248861db189676f8e8544fc54790ab1b23b9e2cd48894. A recorded digest alone does not establish that the linked file matches it.

The website does not fetch the file. Verify the declared digest recipe before relying on it: SDK item digests use canonical JSON of the item array, not the raw pretty-printed file bytes.

No readable study input pairs are stored inline in this receipt. This does not mean the experiment used none.

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.

Absolute arm results

{
    "english": 0.55389999999999994795274460557266138494014739990234375,
    "ainglish": 0.315199999999999980193621240687207318842411041259765625,
    "chance": 0.125
}

Reader or tokenizer results

[
    {
        "model": "gemma3-12b-opaque-choice-q4_k_m",
        "value": -18.1099999999999994315658113919198513031005859375,
        "precision": "q4_k_m"
    },
    {
        "model": "mistral-small3.2-24b-opaque-choice-q4_k_m",
        "value": -27.379999999999999005240169935859739780426025390625,
        "precision": "q4_k_m"
    }
]

Condition results

[
    {
        "id": "choose-any",
        "weight": 1,
        "share": 0.5,
        "value": -15,
        "value_lo": null,
        "value_hi": null,
        "arms": {
            "english": 0.33750000000000002220446049250313080847263336181640625,
            "ainglish": 0.1875,
            "chance": 0.125
        },
        "resolution_bound": "resolvable"
    },
    {
        "id": "draw-uniform",
        "weight": 1,
        "share": 0.5,
        "value": -32.74000000000000198951966012828052043914794921875,
        "value_lo": null,
        "value_hi": null,
        "arms": {
            "english": 0.7702999999999999847233311811578460037708282470703125,
            "ainglish": 0.442900000000000015898393712632241658866405487060546875,
            "chance": 0.125
        },
        "resolution_bound": "resolvable"
    }
]

Exact result and immutable specificationExperiment history

Attempt 38a2871a-23f5-4975-abe2-270ec6567620
Content 04eb391ddfc4e788724e2b65a9aebc2ca61f8f4b02a50bb3b933b6f9a3b48977

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.