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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First result: full recordtoken cost · 2 tokens per declared item

First result · 2026-09-07 11:48 UTC

replace(old=…, new=…) — which thing leaves, and which takes its place?

Not yet counting in evidence decisions. This row remains available for assessment, but does not currently carry a counting evidence result.

What was measured
token cost · token_delta
How does the wording change tokenizer units for the declared tokenizer population?
Reported result
2 tokens per declared item
Reported interval: -3.375 to 2.

More 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
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
Disputed

Eligible replications disagree and this original does not hold a settlement majority.

Is this result within the cost allowance?
This headline is outside the allowance. The reported difference is 2 tokens; the current declaration allows at most 0 tokens.

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?
Disputed.

Eligible replications disagree and this original does not hold a settlement majority.

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 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
Remove the active key from its current role and put the backup key in that role instead.
Ainglish input
replace(old=active-key, new=backup-key).

Input 2

English input
It is required that parser version 2 cease to be the active parser and parser version 3 become active instead.
Ainglish input
must-as-rule replace(old=parser-v2, new=parser-v3).

Input 3

English input
Nia proposes removing pump A from the scoped position and installing pump B there; this is only a proposal.
Ainglish input
proposal-by(Nia): replace(old=pump-A, new=pump-B).

Input 4

English input
Swap the primary database for the replica in the routing table.
Ainglish input
replace(old=primary-db, new=replica-db).

Input 5

English input
The config demands that the old certificate be retired and the new certificate take its place.
Ainglish input
must-as-rule replace(old=old-cert, new=new-cert).

Input 6

English input
Sam proposes replacing the legacy handler with the new handler in the chain.
Ainglish input
proposal-by(Sam): replace(old=legacy-handler, new=new-handler).

Recorded input digest: c107e8861f662ecae7a9942c3f2bd601dca021307cb12098b9637c95b06c3883

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
Compared with
token_delta
Tested population
cl100k_base/o200k_base/p50k_base
Unit tested
pair
How results combine
maximum tokenizer mean

These are the study author’s declarations. A finding applies to this tested scope; this summary does not establish that another study is comparable.

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": -3.375
    },
    {
        "model": "o200k_base",
        "value": -3.25
    },
    {
        "model": "p50k_base",
        "value": 2
    }
]

Condition results

Not recorded

Exact result and immutable specificationExperiment history

Attempt 486d6ac5-9daf-4b46-9e0f-0c72199e1bd4
Content f7bca7aac8e3e3c0996f4d2757c1dc5b88cb85ee31c2df05837562555ad8bb46

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.