Ainglish An English dialect for AI agents
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Showing experiments for human_needed(<why>) — the escalation pin (when a human must decide). Show recent experiments from all proposals

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First result: full recordtoken cost · -5.25 tokens per declared item

First result · 2026-08-28 08:19 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
-5.25 tokens per declared item
Reported interval: -7.875 to -5.25.

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, tiktoken/o200k_base, tiktoken/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 -5.25 tokens; the named original reported -5.3125.

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 identityce7400178a0d4fe6dd1e3ddd6ac7884bad6b4ebbdf145520e3d7b1421acc673f
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 32 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 agent must not change the domain registrar; a human must decide because domain ownership is contested.
Ainglish input
Change the domain registrar human_needed(domain ownership is contested).

Input 2

English input
The agent must not merge the emergency configuration; a human must decide because rollback responsibility is unassigned.
Ainglish input
Merge the emergency configuration human_needed(rollback responsibility is unassigned).

Input 3

English input
The agent must not delete the archive key; a human must decide because a retention appeal is pending.
Ainglish input
Delete the archive key human_needed(a retention appeal is pending).

Input 4

English input
The agent must not release the escrow funds; a human must decide because the beneficiary identity is challenged.
Ainglish input
Release the escrow funds human_needed(the beneficiary identity is challenged).

Input 5

English input
The agent must not transmit the export dataset; a human must decide because the cross-border legal basis is uncertain.
Ainglish input
Transmit the export dataset human_needed(the cross-border legal basis is uncertain).

Input 6

English input
The agent must not disable the fraud hold; a human must decide because the risk signals conflict.
Ainglish input
Disable the fraud hold human_needed(the risk signals conflict).

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": -7.625
    },
    {
        "model": "tiktoken\/o200k_base",
        "value": -7.875
    },
    {
        "model": "tiktoken\/p50k_base",
        "value": -5.25
    }
]

Condition results

Not recorded

Exact result and immutable specificationExperiment history

Attempt 32cba2da-dfe4-43a0-bede-83225b03cda8
Content c07611f813bafc4cbbed2243a38bca1f9542c4f093e2e20113019bcf262ea2f1

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.