comprehension accuracy
How does the wording change correct answers from the declared reader panel?
comprehension_accuracy_delta · reader panel
← each-group / groups-combined — did the result hold in every group, or only after pooling them?
Measurement result
Comprehension accuracy (Δ)
-43.04 percentage points
Reported interval: -52.006 to -34.0534
Server-replayed item bootstrap ·
192 items ·
384 scored/dead cells ·
receipt 2036e4739699….
The complete attestation is in the JSON record.
The result is on the harmful side of this metric's neutral point.
Protocol key comprehension_accuracy_delta · Δ accuracy, pp
Understanding, not just improvement
- English comparison
- 97.27%
97.27% - Ainglish version
- 54.23%
54.23%
These are reported test-item accuracies with any declared condition weights applied, not calibration scores. A positive difference can still hide a poorly understood distinction.
No separate condition accuracy is available here. That does not mean every condition succeeded.
Current evidence step: Re-read the target original and proposal because this filing may have changed their current settlement or lifecycle route.
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?
Complete-pair freshness is not available for this receipt.
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 identity
92d85061748d813965520e6be3f6e57e1c8549fe65d98f2407f86c94b565e293manifest 18b5b75cfb4304df76384f986f7e3167425fde147d3a5a74cf911c80be625766
by Dexagon · 2026-09-04 10:30 UTC ·
NOT disjoint from proposer at submission
(same identity) ·
JSON
Compared with what, and under which conditions?
- What this test is intended to answer
- Test purpose not explicitly declared
Declared by the experiment’s author. This label neither certifies claim coverage nor changes validity, settlement or readiness. A diagnostic can still expose genuine harm.
- English comparison
- Complete, careful English
Declared by the submitter; not a certification that the two inputs preserve the same information.
- Reader exposure
- Reader exposure not recorded as a structured label. A visible reference is not training the model’s weights; future Ainglish-trained performance remains unmeasured.
- Condition coverage
- 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.
Inspect the declared comparison and reader scope
Comparison label: complete-careful-english-v1
Exposure label: Not recorded
Reader population: Not recorded
These are the submitter’s declarations, not a certification that the comparison is fair. Bare wording, complete English and visible-reference studies answer different questions; do not pool them by metric name alone.
Inspect externally stored 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.
The input material is linked externally. The number of study items and controls in that file has not been checked by this website. “External file” does not mean zero inputs.
Open the declared external input artifact. This is an unverified external link, not a hosted or inspected copy.
Declared input digest: 912af384ee1789c29e1b80b462bdd2a830764439f1c2e36110072cd821870c8a. A recorded digest alone does not establish that the linked file matches it.
The website does not fetch the file. Verify the declared digest recipe before relying on it: SDK item digests use canonical JSON of the item array, not the raw pretty-printed file bytes.
No readable study input pairs are stored inline in this receipt. This does not mean the experiment used none.
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.
How to read this receipt
Opposes
The value falls on the registered harmful side of this metric’s neutral point.
A reader-panel result does not establish token savings or performance for models outside its declared population.Disagrees with the named original
This eligible row adds one disagreement. An adverse or null direction is a valid result and remains visible.
Re-read the target original and proposal because this filing may have changed their current settlement or lifecycle route.One receipt, not the whole decision
No single row ratifies or rejects a proposal. Settlement, every declared metric, deterministic gates and the public ballot remain separate.
This result applies to the declared reader population and exposure conditions. Models outside that population, including future Ainglish-trained models, remain unmeasured.What was tested, and how much?
Test questions measure the language claim. Calibration questions check the instrument; they are not extra evidence for that claim.
- Planned test questions
- 192
- Planned calibration questions
- 16
- Planned test responses
- 384
- Planned calibration responses
- 64
Actual scored test responses: Careful English 183; Ainglish 201. These counts exclude calibration and missing responses.
Repeated questions and multiple readers do not automatically create independent observations. Use the study’s sampling and uncertainty method, not a pooled response count, to judge precision.
Reported transport: faults 0; truncated responses 0. Missing or conflicting receipts do not mean zero.
Uncertainty and sample
Reported item-bootstrap interval: -52.006 to -34.0534 percentage points.
This interval concerns the difference, not separate uncertainty bounds for either accuracy. It does not measure uncertainty across humans or future models.
Real cases: 192 · Named readers: 2. These are different units; multiple answers to one case are not new cases.
Panel
Neff 2 · declared reader count; reader independence is not server-validated
mistral-small3.2-24b-opaque-choice-q4_k_m@q4_k_m · gemma3-12b-opaque-choice-q4_k_m@q4_k_m
Exact accuracy grid: 183 English cells · 201 Ainglish cells · attainable delta step 0.0082 percentage points (100/12261).
| Reader or tokenizer | Reported value |
|---|---|
mistral-small3.2-24b-opaque-choice-q4_k_m @q4_k_m |
-44.44 |
gemma3-12b-opaque-choice-q4_k_m @q4_k_m |
-42.26 |
Replication chain
This row is itself a replication of 92d85061748d….
No replications yet. Independent confirmation needs an eligible party to repeat the same test design with wholly fresh complete inputs. The live comparison contract decides agreement; a new seed or reader over the same inputs is not fresh-input confirmation.
Inspect the original manifest — exact, re-runnable specification
These are the committed bytes rendered as readable JSON. Expanding this audit detail does not change the measurement’s current status.
{
"construct": "each-group / groups-combined",
"metric": "comprehension_accuracy_delta",
"seed": 2026090411,
"comparator": {
"kind": "complete-careful-english-v1"
},
"items_sha256": "912af384ee1789c29e1b80b462bdd2a830764439f1c2e36110072cd821870c8a",
"items_url": "https://raw.githubusercontent.com/dexagon-ai/ainglish-evidence/c322fef54a77684b40731fba767fa395fde32dee/dispute-settlement-wave-v1-2026-09-04/group-scope.items.json",
"models": [
"mistral-small3.2-24b-opaque-choice-q4_k_m@q4_k_m",
"gemma3-12b-opaque-choice-q4_k_m@q4_k_m"
],
"readers": [
{
"name": "mistral-small3.2-24b-opaque-choice-q4_k_m",
"provider": "ollama",
"model": "dexagon-mistral-small3.2-24b-pp-task:ctx4k",
"precision": "q4_k_m",
"api": "openai",
"base_url": "http://127.0.0.1:11434/v1",
"model_digest": "sha256:6629ee92de51c9a1367e1331cfa9ef6a77058a44a6a3e18ab524b2d0404252de",
"digest_source": "ollama:/api/tags",
"instrument_preparation": {
"entry_point": "prepare_reader_instruments",
"binding": "ollama:/api/tags"
},
"answer_protocol": "opaque-choice-v1",
"max_tokens": 32,
"timeout_s": 120,
"temperature": 0,
"seed": 2026090411,
"top_p": "provider-default",
"top_k": "provider-default",
"num_ctx": "provider-default",
"reasoning_effort": "provider-default"
},
{
"name": "gemma3-12b-opaque-choice-q4_k_m",
"provider": "ollama",
"model": "dexagon-gemma3-12b-pp-task:ctx4k",
"precision": "q4_k_m",
"api": "openai",
"base_url": "http://127.0.0.1:11434/v1",
"model_digest": "sha256:de1f65ea3438dfcc7c3387802b9425a140fb01ecc79edf4924a13fab051eb68f",
"digest_source": "ollama:/api/tags",
"instrument_preparation": {
"entry_point": "prepare_reader_instruments",
"binding": "ollama:/api/tags"
},
"answer_protocol": "opaque-choice-v1",
"max_tokens": 32,
"timeout_s": 120,
"temperature": 0,
"seed": 2026090411,
"top_p": "provider-default",
"top_k": "provider-default",
"num_ctx": "provider-default",
"reasoning_effort": "provider-default"
}
],
"instrument_preparation": {
"entry_point": "prepare_reader_instruments",
"binding": [
{
"reader": "mistral-small3.2-24b-opaque-choice-q4_k_m@q4_k_m",
"digest_source": "ollama:/api/tags"
},
{
"reader": "gemma3-12b-opaque-choice-q4_k_m@q4_k_m",
"digest_source": "ollama:/api/tags"
}
]
},
"item_counts": {
"real": 192,
"calibration": 16
},
"interval_kind": "bootstrap_items",
"interval_estimator": {
"kind": "ainglish.panel.bootstrap-items-attestation.v1",
"algorithm": "sha256-counter-modulo-v1",
"draws": 2000,
"sampling_unit": "item",
"quantiles": [
"0.025",
"0.975"
],
"items_index_sha256": "e9c750de4e50837b5d3285b7e9539cf76e9020e9982371306cdc5184bcac5267"
},
"accuracy_resolution": {
"unit": "percentage_points",
"scored_cells": {
"english": 183,
"ainglish": 201
},
"one_cell_pp": {
"english": "0.5464",
"ainglish": "0.4975"
},
"delta_grid": {
"numerator_pp": 100,
"denominator_lcm": 12261,
"step_pp": "0.0082"
}
},
"calibration": {
"planted_arm": "ainglish",
"min_gap": 0.5,
"min_recovered": null,
"rule": "absolute-gap-v1",
"ordering": "calibration-first",
"arm_exposure": "both-arms-per-reader-item",
"cells": 64
},
"difficulty": {
"annotated": false
},
"harness": "ainglish-panel/0.2.52",
"transport": {
"mistral-small3.2-24b-opaque-choice-q4_k_m@q4_k_m": {
"max_tokens": 32,
"timeout_s": 120,
"temperature": 0,
"seed": 2026090411,
"top_p": "provider-default",
"top_k": "provider-default",
"num_ctx": "provider-default",
"reasoning_effort": "provider-default"
},
"gemma3-12b-opaque-choice-q4_k_m@q4_k_m": {
"max_tokens": 32,
"timeout_s": 120,
"temperature": 0,
"seed": 2026090411,
"top_p": "provider-default",
"top_k": "provider-default",
"num_ctx": "provider-default",
"reasoning_effort": "provider-default"
}
},
"concurrency": {
"max_in_flight": 1,
"per_reader_max_in_flight": {
"mistral-small3.2-24b-opaque-choice-q4_k_m": 1,
"gemma3-12b-opaque-choice-q4_k_m": 1
},
"result_order": "deterministic-plan-order",
"calibration_barrier": true,
"automatic_retries": false
},
"transport_faults": {
"total": 0,
"retried": false,
"per_cell": []
},
"transport_truncations": {
"total": 0,
"per_reader_cell": [],
"by_cell": {
"english": 0,
"ainglish": 0
},
"imbalanced_across_cells": false
},
"protocol": "panel.py counterbalanced real arms + both-arms-per-reader-item planted-effect calibration gate"
}