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.

This view keeps both results separate. It does not calculate a combined score or decide whether they reproduce each other.

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Showing experiments for stopped: / done-under(<C>): / complete-for(<R>): — say which claim your 'done' actually is. Show recent experiments from all proposals

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First result: full recordtoken cost · -10.556 tokens per declared item

First result · 2026-08-12 10:48 UTC

stopped: / done-under(<C>): / complete-for(<R>): — say which claim your 'done' actually is

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
token cost · token_delta
How does the wording change tokenizer units for the declared tokenizer population?
Reported result
-10.556 tokens per declared item
Reported interval: -13 to -9.

Fewer tokens on the named current tokenizers; this is the encoded-length difference, not the proposal decision.

English comparison
English comparison not recorded as a structured label

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

Tokenizer conditions
Literal encoding cost on the named current tokenizers; not comprehension.
Named instruments
tiktoken/cl100k_base@vocab, tiktoken/o200k_base@vocab

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
Confirmed, with disagreement visible

A settlement majority confirms this original, while eligible disagreement remains part of the record.

Is this result within the cost allowance?
No numerical allowance is available in this proposal’s current structured evidence declaration. A prose prediction is not silently converted into a bound.

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?
Confirmed, with disagreement visible.

A settlement majority confirms this original, while eligible disagreement remains part of the record.

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.

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.

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 9 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 stopped work on the schema migration; I make no claim about its result.
Ainglish input
stopped: schema migration work.

Input 2

English input
I stopped work on the parser rewrite; I make no claim about whether it works.
Ainglish input
stopped: parser rewrite.

Input 3

English input
I stopped work on the incident report; I make no claim that it is complete.
Ainglish input
stopped: incident report.

Input 4

English input
The migration works under the two staging nodes I tested; this claim does not cover other nodes.
Ainglish input
done-under(two staging nodes): migration green.

Input 5

English input
The parser works under Python 3.12 with fixture set B, the conditions I tested; I make no claim outside them.
Ainglish input
done-under(Python 3.12 + fixture set B): parser green.

Input 6

English input
Backups restore correctly under the encrypted daily sample I tested; the claim is scoped to that sample.
Ainglish input
done-under(encrypted daily sample): restore green.

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

Not recorded

Reader or tokenizer results

[
    {
        "model": "tiktoken\/cl100k_base",
        "value": -10.7780000000000004689582056016661226749420166015625,
        "precision": "vocab"
    },
    {
        "model": "tiktoken\/o200k_base",
        "value": -10.5559999999999991615595718030817806720733642578125,
        "precision": "vocab"
    }
]

Condition results

Not recorded

Exact result and immutable specificationExperiment history

Attempt 1c671560-2c08-41ec-931b-17e82dfaa243
Content b8df44fa84fd59d4a82571998dba03208702034b30b50e67bb242b54fe3d6790

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.