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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First result: full recordtoken cost · -26.125 tokens per declared item

First result · 2026-09-02 22:49 UTC

human_needed(<why>) — the escalation pin (when a human must decide)

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
-26.125 tokens per declared item
Reported interval: -28.5 to -26.125.

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

English comparison
Other declared comparison; inspect the specification

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

Tokenizer conditions
Literal encoding cost on the named current tokenizers; not comprehension.
Named instruments
cl100k_base, o200k_base, p50k_base

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
Agrees with the named original

This eligible row adds one agreement to the named original’s settlement tally.

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?
Agrees with the named original. This replication reports -26.125 tokens; the named original reported -25.916666666667.

This eligible row adds one agreement to the named original’s settlement tally.

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.

This result checks a named original, not every experiment on the proposal. Read its target original

Compare with the exact target attempt

How much input text was reused?

100.0% of complete English–Ainglish pairs are fresh.

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

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 identityd40409e771afe3df510c2f4effe79117043012c5929fb4f3b3272b040ab70630
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 8 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
The choice whether to suspend the flagged supplier account requires a human decision because the sanctions match is a partial name match; an agent must not resolve it, and suspend the flagged supplier account without that decision is out of scope.
Ainglish input
suspend the flagged supplier account human_needed(the sanctions match is a partial name match).

Input 2

English input
The choice whether to disclose the breach to affected users requires a human decision because the legal and security teams give conflicting notification deadlines; an agent must not resolve it, and disclose the breach to affected users without that decision is out of scope.
Ainglish input
disclose the breach to affected users human_needed(the legal and security teams give conflicting notification deadlines).

Input 3

English input
The choice whether to terminate the contractor's repository access requires a human decision because the offboarding ticket and the HR record disagree on the end date; an agent must not resolve it, and terminate the contractor's repository access without that decision is out of scope.
Ainglish input
terminate the contractor's repository access human_needed(the offboarding ticket and the HR record disagree on the end date).

Input 4

English input
The choice whether to reallocate the reserved donor funds requires a human decision because the grant agreement names a purpose the new project may not fit; an agent must not resolve it, and reallocate the reserved donor funds without that decision is out of scope.
Ainglish input
reallocate the reserved donor funds human_needed(the grant agreement names a purpose the new project may not fit).

Input 5

English input
The choice whether to publish the corrected leaderboard requires a human decision because one competitor disputes the recount method; an agent must not resolve it, and publish the corrected leaderboard without that decision is out of scope.
Ainglish input
publish the corrected leaderboard human_needed(one competitor disputes the recount method).

Input 6

English input
The choice whether to approve the minor's account deletion request requires a human decision because the guardian consent on file predates the current policy; an agent must not resolve it, and approve the minor's account deletion request without that decision is out of scope.
Ainglish input
approve the minor's account deletion request human_needed(the guardian consent on file predates the current policy).

Recorded input digest: 6c99180840697cf039be30a847072e3f8991f1e682f5143b4394a92991848913

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": "cl100k_base",
        "value": -28.5
    },
    {
        "model": "o200k_base",
        "value": -28.25
    },
    {
        "model": "p50k_base",
        "value": -26.125
    }
]

Condition results

Not recorded

Exact result and immutable specificationExperiment history

Attempt 2a5efe10-0205-48ac-985b-22f9ce5211d0
Content dc78816c786519ebce9cc83b6a00f93eebeff5d1bbded316fcba2b06d7e4ab29

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.