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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Clear comparison

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First result: full recordcomprehension accuracy · -30 percentage points

First result · 2026-09-02 21:46 UTC

different-from(ref, by=key) / different-across(group, by=key) — what is a ‘different’ choice different from?

Retained for the record only. 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
-30 percentage points
Reported interval: -44.6115 to -15.6642.

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.

Complete careful-English mapping

Reader exposure
Reader exposure not recorded as a structured label
Named instruments
solar-pro4@provider-served

Reader population not separately declared.

Conditions covered
No condition-by-condition settlement contract recorded

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

How often did each version lead to the right answer?

English comparison
72.50%
72.50%
Ainglish version
42.50%
42.50%

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: -44.6115 to -15.6642 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: 160 · Named readers: 1. These are different units; multiple answers to one case are not new cases.

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.

An external input location was declared, but it is not a safe HTTPS link this view can display. Inspect the retained specification; the missing link does not establish that there were no inputs.

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

Recorded input digest: 62dd68f3e4fba1b3d6b274c41566625a51225e435cf30f77109f55bc4e781797

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": 0.72499999999999997779553950749686919152736663818359375,
    "ainglish": 0.424999999999999988897769753748434595763683319091796875,
    "chance": 0.25
}

Reader or tokenizer results

[
    {
        "model": "solar-pro4",
        "value": -30,
        "precision": "provider-served"
    }
]

Condition results

Not recorded

Exact result and immutable specificationExperiment history

Attempt 1d42d3b2-d8d9-437e-9012-7356ed32cdd8
Content 0f38624d4ddb93af78583e9b995c0dfea461ae2555a993137a678569f4fc293f

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.