Ainglish An English dialect for AI agents

Evidence

Compare two experiments

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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Clear comparison

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First result: full recordcomprehension accuracy · 53.77 percentage points

First result · 2026-08-29 18:46 UTC

they-one / they-many — say whether ‘they’ is one actor or several

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

What was measured
comprehension accuracy · comprehension_accuracy_delta
How does the wording change correct answers from the declared reader panel?
Historical reported result
53.77 percentage points
Reported interval: 47.155 to 60.955.

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

English comparison
Other declared comparison; inspect the specification

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

Reader exposure
Reader exposure not recorded as a structured label
Named instruments
deepseek-flash-remote@provider-served

Reader population not separately declared.

Conditions covered
Separate outcomes retained for all 2 declared conditions

one, many

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.

How often did each version lead to the right answer?

English comparison
41.67%
41.67%
Ainglish version
95.44%
95.44%

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 interval (method not identified here): 47.155 to 60.955 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: 192 · Named readers: 1. 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
one98.28 Not recorded 0.00%98.28%
many9.26 Not recorded 83.33%92.59%

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.

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

Recorded input digest: 8417e8bf936eb47ebf3c6d2869aa50da32bdc4ad80b6c3f9dde309157a926160

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": 0.416700000000000014832579608992091380059719085693359375,
    "ainglish": 0.95440000000000002611244553918368183076381683349609375,
    "chance": 0.333299999999999985167420391007908619940280914306640625
}

Reader or tokenizer results

[
    {
        "model": "deepseek-flash-remote",
        "value": 53.77000000000000312638803734444081783294677734375,
        "precision": "provider-served"
    }
]

Condition results

[
    {
        "id": "one",
        "weight": 1,
        "share": 0.5,
        "value": 98.280000000000001136868377216160297393798828125,
        "value_lo": null,
        "value_hi": null,
        "arms": {
            "english": 0,
            "ainglish": 0.98280000000000000692779167366097681224346160888671875,
            "chance": 0.333299999999999985167420391007908619940280914306640625
        },
        "resolution_bound": "resolvable"
    },
    {
        "id": "many",
        "weight": 1,
        "share": 0.5,
        "value": 9.2599999999999997868371792719699442386627197265625,
        "value_lo": null,
        "value_hi": null,
        "arms": {
            "english": 0.83330000000000004067857162226573564112186431884765625,
            "ainglish": 0.925899999999999945288209346472285687923431396484375,
            "chance": 0.333299999999999985167420391007908619940280914306640625
        },
        "resolution_bound": "resolvable"
    }
]

Exact result and immutable specificationExperiment history

Attempt 34fb600b-d7a1-49e0-9cf2-79bc29287a4d
Content 3b3e84445e1d4451516a7a48af85d138b6358d05acad7a2d883ef59f665d3487

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.