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 · -7.5 percentage points

First result · 2026-10-16 12:44 UTC

number-provenance — counted(<N>) / estimated(<N>) / quoted(<N>|<source>) / placeholder(<N>): a quantity declares where it came from

Counts in current evidence decisions. This row currently contributes to evidence decisions. Its direction is separate from whether the proposal is ready for adoption.

What was measured
comprehension accuracy · comprehension_accuracy_delta
How does the wording change correct answers from the declared reader panel?
Reported result
-7.5 percentage points
Reported interval: -13 to -2.6316.

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.

Same comparator kind as the source. Both arms carry the identical header line, the identical target label and the identical figure; only the provenance statement differs. The English arm is the registered english_mapping per form with <N> and <source> substituted; the marked arm is the cold marker form with no teaching and no legend. Calibration items are separate and use this construct's own vocabulary.

Reader exposure
Reader exposure not recorded as a structured label
Named instruments
deepseek-flash

Reader population not separately declared.

Conditions covered
Separate outcomes retained for all 4 declared conditions

counted, estimated, quoted, placeholder

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
Disagrees with the named original

This eligible row adds one disagreement. An adverse or null direction is a valid result and remains visible.

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?

Complete-pair freshness is not available for this receipt.

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

The item banks are referenced by digest rather than inspectable here. Compare the explicitly retrieved, digest-verified files before making a freshness claim.

Declared item-bank digests: different. This compares bank identity, not shared sentences; different bank digests can still contain identical pairs.

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 identity1ad6d293a6e0cebfdef3016c85c2be873336aca8b1fa1bf1f0bc25758d1f14b1

How often did each version lead to the right answer?

English comparison
100.00%
100.00%
Ainglish version
92.50%
92.50%

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: -13 to -2.6316 percentage points.

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

At least one declared condition is resolution-limited. The overall interval does not settle every condition.

Real cases: 160 · 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
counted-30 Not recorded 100.00%70.00%
estimated0 Not recorded 100.00%100.00%
quoted0 Not recorded 100.00%100.00%
placeholder0 Not recorded 100.00%100.00%

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: 2def90b8fe0bc03232c298fc7d6ca5c953be675fe2acd7eedbb6f389eaeeca79. 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": 1,
    "ainglish": 0.9250000000000000444089209850062616169452667236328125,
    "chance": 0.200000000000000011102230246251565404236316680908203125
}

Reader or tokenizer results

[
    {
        "model": "deepseek-flash",
        "value": -7.5
    }
]

Condition results

[
    {
        "id": "counted",
        "weight": 1,
        "share": 0.25,
        "value": -30,
        "value_lo": null,
        "value_hi": null,
        "arms": {
            "english": 1,
            "ainglish": 0.6999999999999999555910790149937383830547332763671875,
            "chance": 0.200000000000000011102230246251565404236316680908203125
        },
        "resolution_bound": "resolvable"
    },
    {
        "id": "estimated",
        "weight": 1,
        "share": 0.25,
        "value": 0,
        "value_lo": null,
        "value_hi": null,
        "arms": {
            "english": 1,
            "ainglish": 1,
            "chance": 0.200000000000000011102230246251565404236316680908203125
        },
        "resolution_bound": "ceiling"
    },
    {
        "id": "quoted",
        "weight": 1,
        "share": 0.25,
        "value": 0,
        "value_lo": null,
        "value_hi": null,
        "arms": {
            "english": 1,
            "ainglish": 1,
            "chance": 0.200000000000000011102230246251565404236316680908203125
        },
        "resolution_bound": "ceiling"
    },
    {
        "id": "placeholder",
        "weight": 1,
        "share": 0.25,
        "value": 0,
        "value_lo": null,
        "value_hi": null,
        "arms": {
            "english": 1,
            "ainglish": 1,
            "chance": 0.200000000000000011102230246251565404236316680908203125
        },
        "resolution_bound": "ceiling"
    }
]

Exact result and immutable specificationExperiment history

Attempt 7e8a8d55-9957-4038-90a5-5171893caedd
Content efdd5eccd61c5e89ff03dc46c031cb4b9c4a80f5834e4aa3ffd1b3236c96db7e

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.