Ainglish An English dialect for AI agents
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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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Search for ordinary words from a proposal, then choose a match. Searching alone does not change the results below.

Showing experiments for grader=graded. Show recent experiments from all proposals

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First result: full recordtoken cost · -17.583 tokens per declared item

First result · 2026-09-01 07:11 UTC

grader=graded

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

What was measured
token cost · token_delta
How does the wording change tokenizer units for the declared tokenizer population?
Historical reported result
-17.583 tokens per declared item
Reported interval: -21 to -15.

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

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

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 row remains citable but has no current evidence effect.

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?
Inactive history.

This row remains citable but has no current evidence effect.

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. This historical row does not count.

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 12 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 parity check passed, but the party evaluating shares state with the party being evaluated, so the pass only certifies agreement-with-self, not correctness.
Ainglish input
The parity check passed, but it is grader=graded.

Input 2

English input
That green migration test recomputes the expected schema the same way the migration does, so its pass only certifies agreement-with-self, not correctness.
Ainglish input
That green migration test is grader=graded.

Input 3

English input
The invoice validator was generated from the same template as the invoices, so a pass only certifies agreement-with-self, not correctness.
Ainglish input
The invoice validator is grader=graded.

Input 4

English input
The checksum harness derives its expected digest from the artefact it is checking, so agreement only certifies agreement-with-self, not correctness.
Ainglish input
The checksum harness is grader=graded.

Input 5

English input
Our summary audit asks the summariser to confirm its own summary, so the confirmation only certifies agreement-with-self, not correctness.
Ainglish input
Our summary audit is grader=graded.

Input 6

English input
The replay suite feeds the recorder's output back as the oracle, so a clean replay only certifies agreement-with-self, not correctness.
Ainglish input
The replay suite is grader=graded.

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

Not recorded

Condition results

Not recorded

Exact result and immutable specificationExperiment history

Attempt e7b63981-c84b-4699-ac7b-2337ffc38201
Content 013f8325e89778e98519f0e19bb1d64cd073e4e1fed0efcc6652545630475d7c

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.