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Cliste experiment
Partial cue confidence in continuing VSA memory
Registered 9 Oct 2026 · Snapshot 12 Oct 2026
Execution: completedScientific verdict: partial confidence not met
The question
Test whether a fixed support-dependent acceptance rule improves usable partial-cue recall without introducing wrong-source acceptance, preserving VSA content, rankings and the continuing life.
Why this experiment?
A partial cue can resemble the wrong memory. Recall needs to recover useful content while recognizing when the evidence is insufficient.
How it works
Compare partial-cue recall with familiar and unfamiliar inputs, retaining incorrect accepts and refusals. Cleanup and confidence rules are qualified before relying on them for a remembered continuation.
Follow this part of the investigation: More answers are useful only if the extra confidence is justified →Implementation and comparison details
One shared native query per field; compare baseline, support-scaled thresholds and a fixed partial boost. Thirty-two new native images enter a 47-to-79 encounter fork; sixty-four independent sources never enter memory.
What happened
Completed valid negative qualification: support-scaled correct partial recall 111/192 versus baseline 66/192 (gain 23.44 percentage points; source-bootstrap 95% interval 17.71–29.17). No wrong present-source accepts. Never-admitted partial false accepts 7/384 versus 5/384; two sources gain an additional erroneous field, giving a 9.51% upper bound above the required 5%. Both sources already had baseline errors elsewhere. Fixed boost gives 121 correct and 18 novel wrong fields. All independent and continuity audits pass; full 1.3 stays open.
Measurements
| Recorded field | Value |
|---|---|
| primary | {
"mean": 0.234375,
"ci95": [
0.17708333333333334,
0.2916666666666667
],
"sources": 32,
"samples": 10000,
"seed": 1403,
"per_source": [
{
"source_index": 0,
"fields": 6,
"baseline": 0.3333333333333333,
"candidate": 0.5,
"difference": 0.16666666666666669,
"introduced": true
},
{
"source_index": 1,
"fields": 6,
"baseline": 0.6666666666666666,
"candidate": 1,
"difference": 0.33333333333333337,
"introduced": true
},
{
"source_index": 2,
"fields": 6,
"baseline": 0.16666666666666666,
"candidate": 0.8333333333333334,
"difference": 0.6666666666666667,
"introduced": true
},
{
"source_index": 3,
"fields": 6,
"baseline": 0,
"candidate": 0.3333333333333333,
"difference": 0.3333333333333333,
"introduced": true
},
{
"source_index": 4,
"fields": 6,
"baseline": 0.5,
"candidate": 0.8333333333333334,
"difference": 0.33333333333333337,
"introduced": true
},
{
"source_index": 5,
"fields": 6,
"baseline": 0.6666666666666666,
"candidate": 1,
"difference": 0.33333333333333337,
"introduced": true
},
{
"source_index": 6,
"fields": 6,
"baseline": 0.5,
"candidate": 1,
"difference": 0.5,
"introduced": true
},
{
"source_index": 7,
"fields": 6,
"baseline": 0.3333333333333333,
"candidate": 0.5,
"difference": 0.16666666666666669,
"introduced": true
},
{
"source_index": 8,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 9,
"fields": 6,
"baseline": 0.6666666666666666,
"candidate": 1,
"difference": 0.33333333333333337,
"introduced": true
},
{
"source_index": 10,
"fields": 6,
"baseline": 0.6666666666666666,
"candidate": 1,
"difference": 0.33333333333333337,
"introduced": true
},
{
"source_index": 11,
"fields": 6,
"baseline": 0.3333333333333333,
"candidate": 0.3333333333333333,
"difference": 0,
"introduced": false
},
{
"source_index": 12,
"fields": 6,
"baseline": 0,
"candidate": 0.3333333333333333,
"difference": 0.3333333333333333,
"introduced": true
},
{
"source_index": 13,
"fields": 6,
"baseline": 0,
"candidate": 0.16666666666666666,
"difference": 0.16666666666666666,
"introduced": true
},
{
"source_index": 14,
"fields": 6,
"baseline": 0.16666666666666666,
"candidate": 0.3333333333333333,
"difference": 0.16666666666666666,
"introduced": true
},
{
"source_index": 15,
"fields": 6,
"baseline": 0.3333333333333333,
"candidate": 0.5,
"difference": 0.16666666666666669,
"introduced": true
},
{
"source_index": 16,
"fields": 6,
"baseline": 0.3333333333333333,
"candidate": 0.3333333333333333,
"difference": 0,
"introduced": false
},
{
"source_index": 17,
"fields": 6,
"baseline": 0.5,
"candidate": 1,
"difference": 0.5,
"introduced": true
},
{
"source_index": 18,
"fields": 6,
"baseline": 0.16666666666666666,
"candidate": 0.5,
"difference": 0.33333333333333337,
"introduced": true
},
{
"source_index": 19,
"fields": 6,
"baseline": 0.3333333333333333,
"candidate": 0.5,
"difference": 0.16666666666666669,
"introduced": true
},
{
"source_index": 20,
"fields": 6,
"baseline": 0.3333333333333333,
"candidate": 0.6666666666666666,
"difference": 0.3333333333333333,
"introduced": true
},
{
"source_index": 21,
"fields": 6,
"baseline": 0.3333333333333333,
"candidate": 0.3333333333333333,
"difference": 0,
"introduced": false
},
{
"source_index": 22,
"fields": 6,
"baseline": 0.16666666666666666,
"candidate": 0.3333333333333333,
"difference": 0.16666666666666666,
"introduced": true
},
{
"source_index": 23,
"fields": 6,
"baseline": 0.5,
"candidate": 0.8333333333333334,
"difference": 0.33333333333333337,
"introduced": true
},
{
"_indexOmission": "8 further items"
}
]
} |
| present errors | {
"introduced_sources": 0,
"per_source": [
{
"source_index": 0,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 1,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 2,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 3,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 4,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 5,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 6,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 7,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 8,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 9,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 10,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 11,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 12,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 13,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 14,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 15,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 16,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 17,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 18,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 19,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 20,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 21,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 22,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 23,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"_indexOmission": "8 further items"
}
]
} |
| novel | {
"sources": 64,
"introduced_sources": 2,
"introduced_source_upper95": 0.09512171935033914,
"field_false_acceptance_difference": {
"mean": 0.005208333333333334,
"ci95": [
0,
0.013020833333333334
],
"sources": 64,
"samples": 10000,
"seed": 1403
},
"baseline_field_false_acceptance": {
"mean": 0.013020833333333332,
"ci95": [
0.0026041666666666665,
0.028645833333333332
],
"sources": 64,
"samples": 10000,
"seed": 1403
},
"candidate_field_false_acceptance": {
"mean": 0.018229166666666664,
"ci95": [
0.0026041666666666665,
0.0390625
],
"sources": 64,
"samples": 10000,
"seed": 1403
},
"per_source": [
{
"source_index": 32,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 33,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 34,
"fields": 6,
"baseline": 0.3333333333333333,
"candidate": 0.5,
"difference": 0.16666666666666669,
"introduced": true
},
{
"source_index": 35,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 36,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 37,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 38,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 39,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 40,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 41,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 42,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 43,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 44,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 45,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 46,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 47,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 48,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 49,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 50,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 51,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"source_index": 52,
"fields": 6,
"baseline": 0.16666666666666666,
"candidate": 0.16666666666666666,
"difference": 0,
"introduced": false
},
{
"source_index": 53,
"fields": 6,
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"difference": 0,
"introduced": false
},
{
"source_index": 54,
"fields": 6,
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"difference": 0,
"introduced": false
},
{
"source_index": 55,
"fields": 6,
"baseline": 0,
"candidate": 0,
"difference": 0,
"introduced": false
},
{
"_indexOmission": "40 further items"
}
]
} |
| totals | {
"before/admitted": {
"12": {
"baseline": {
"fields": 64,
"correct": 0,
"wrong": 0,
"abstained": 64,
"first_rank": 0
},
"support_scaled": {
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"correct": 0,
"wrong": 3,
"abstained": 61,
"first_rank": 0
},
"fixed_partial_boost": {
"fields": 64,
"correct": 0,
"wrong": 3,
"abstained": 61,
"first_rank": 0
}
},
"24": {
"baseline": {
"fields": 64,
"correct": 0,
"wrong": 0,
"abstained": 64,
"first_rank": 0
},
"support_scaled": {
"fields": 64,
"correct": 0,
"wrong": 3,
"abstained": 61,
"first_rank": 0
},
"fixed_partial_boost": {
"fields": 64,
"correct": 0,
"wrong": 5,
"abstained": 59,
"first_rank": 0
}
},
"40": {
"baseline": {
"fields": 64,
"correct": 0,
"wrong": 2,
"abstained": 62,
"first_rank": 0
},
"support_scaled": {
"fields": 64,
"correct": 0,
"wrong": 4,
"abstained": 60,
"first_rank": 0
},
"fixed_partial_boost": {
"fields": 64,
"correct": 0,
"wrong": 7,
"abstained": 57,
"first_rank": 0
}
},
"64": {
"baseline": {
"fields": 64,
"correct": 0,
"wrong": 3,
"abstained": 61,
"first_rank": 0
},
"support_scaled": {
"fields": 64,
"correct": 0,
"wrong": 3,
"abstained": 61,
"first_rank": 0
},
"fixed_partial_boost": {
"fields": 64,
"correct": 0,
"wrong": 3,
"abstained": 61,
"first_rank": 0
}
}
},
"after/admitted": {
"12": {
"baseline": {
"fields": 64,
"correct": 0,
"wrong": 0,
"abstained": 64,
"first_rank": 57
},
"support_scaled": {
"fields": 64,
"correct": 30,
"wrong": 0,
"abstained": 34,
"first_rank": 57
},
"fixed_partial_boost": {
"fields": 64,
"correct": 30,
"wrong": 0,
"abstained": 34,
"first_rank": 57
}
},
"24": {
"baseline": {
"fields": 64,
"correct": 28,
"wrong": 0,
"abstained": 36,
"first_rank": 63
},
"support_scaled": {
"fields": 64,
"correct": 38,
"wrong": 0,
"abstained": 26,
"first_rank": 63
},
"fixed_partial_boost": {
"fields": 64,
"correct": 41,
"wrong": 0,
"abstained": 23,
"first_rank": 63
}
},
"40": {
"baseline": {
"fields": 64,
"correct": 38,
"wrong": 0,
"abstained": 26,
"first_rank": 64
},
"support_scaled": {
"fields": 64,
"correct": 43,
"wrong": 0,
"abstained": 21,
"first_rank": 64
},
"fixed_partial_boost": {
"fields": 64,
"correct": 50,
"wrong": 0,
"abstained": 14,
"first_rank": 64
}
},
"64": {
"baseline": {
"fields": 64,
"correct": 47,
"wrong": 0,
"abstained": 17,
"first_rank": 64
},
"support_scaled": {
"fields": 64,
"correct": 47,
"wrong": 0,
"abstained": 17,
"first_rank": 64
},
"fixed_partial_boost": {
"fields": 64,
"correct": 47,
"wrong": 0,
"abstained": 17,
"first_rank": 64
}
}
},
"after/never_admitted": {
"12": {
"baseline": {
"fields": 128,
"correct": 0,
"wrong": 0,
"abstained": 128,
"first_rank": 0
},
"support_scaled": {
"fields": 128,
"correct": 0,
"wrong": 1,
"abstained": 127,
"first_rank": 0
},
"fixed_partial_boost": {
"fields": 128,
"correct": 0,
"wrong": 1,
"abstained": 127,
"first_rank": 0
}
},
"24": {
"baseline": {
"fields": 128,
"correct": 0,
"wrong": 2,
"abstained": 126,
"first_rank": 0
},
"support_scaled": {
"fields": 128,
"correct": 0,
"wrong": 2,
"abstained": 126,
"first_rank": 0
},
"fixed_partial_boost": {
"fields": 128,
"correct": 0,
"wrong": 4,
"abstained": 124,
"first_rank": 0
}
},
"40": {
"baseline": {
"fields": 128,
"correct": 0,
"wrong": 3,
"abstained": 125,
"first_rank": 0
},
"support_scaled": {
"fields": 128,
"correct": 0,
"wrong": 4,
"abstained": 124,
"first_rank": 0
},
"fixed_partial_boost": {
"fields": 128,
"correct": 0,
"wrong": 13,
"abstained": 115,
"first_rank": 0
}
},
"64": {
"baseline": {
"fields": 128,
"correct": 0,
"wrong": 6,
"abstained": 122,
"first_rank": 0
},
"support_scaled": {
"fields": 128,
"correct": 0,
"wrong": 6,
"abstained": 122,
"first_rank": 0
},
"fixed_partial_boost": {
"fields": 128,
"correct": 0,
"wrong": 6,
"abstained": 122,
"first_rank": 0
}
}
}
} |
| fixed control | {
"correct_gain": {
"mean": 0.2864583333333333,
"ci95": [
0.22916666666666669,
0.34375
],
"sources": 32,
"samples": 10000,
"seed": 1403
},
"novel_field_error_gain": {
"mean": 0.033854166666666664,
"ci95": [
0.018229166666666664,
0.054687499999999986
],
"sources": 64,
"samples": 10000,
"seed": 1403
},
"support_minus_fixed_correct": {
"mean": -0.05208333333333333,
"ci95": [
-0.08333333333333334,
-0.02604166666666666
],
"sources": 32,
"samples": 10000,
"seed": 1403
},
"per_source_correct": [
{
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"fields": 6,
"baseline": 0.3333333333333333,
"candidate": 0.8333333333333334,
"difference": 0.5,
"introduced": true
},
{
"source_index": 1,
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"candidate": 1,
"difference": 0.33333333333333337,
"introduced": true
},
{
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"difference": 0.6666666666666667,
"introduced": true
},
{
"source_index": 3,
"fields": 6,
"baseline": 0,
"candidate": 0.3333333333333333,
"difference": 0.3333333333333333,
"introduced": true
},
{
"source_index": 4,
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"baseline": 0.5,
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"difference": 0.33333333333333337,
"introduced": true
},
{
"source_index": 5,
"fields": 6,
"baseline": 0.6666666666666666,
"candidate": 1,
"difference": 0.33333333333333337,
"introduced": true
},
{
"source_index": 6,
"fields": 6,
"baseline": 0.5,
"candidate": 1,
"difference": 0.5,
"introduced": true
},
{
"source_index": 7,
"fields": 6,
"baseline": 0.3333333333333333,
"candidate": 0.5,
"difference": 0.16666666666666669,
"introduced": true
},
{
"source_index": 8,
"fields": 6,
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"candidate": 0.16666666666666666,
"difference": 0.16666666666666666,
"introduced": true
},
{
"source_index": 9,
"fields": 6,
"baseline": 0.6666666666666666,
"candidate": 1,
"difference": 0.33333333333333337,
"introduced": true
},
{
"source_index": 10,
"fields": 6,
"baseline": 0.6666666666666666,
"candidate": 1,
"difference": 0.33333333333333337,
"introduced": true
},
{
"source_index": 11,
"fields": 6,
"baseline": 0.3333333333333333,
"candidate": 0.3333333333333333,
"difference": 0,
"introduced": false
},
{
"source_index": 12,
"fields": 6,
"baseline": 0,
"candidate": 0.3333333333333333,
"difference": 0.3333333333333333,
"introduced": true
},
{
"source_index": 13,
"fields": 6,
"baseline": 0,
"candidate": 0.3333333333333333,
"difference": 0.3333333333333333,
"introduced": true
},
{
"source_index": 14,
"fields": 6,
"baseline": 0.16666666666666666,
"candidate": 0.3333333333333333,
"difference": 0.16666666666666666,
"introduced": true
},
{
"source_index": 15,
"fields": 6,
"baseline": 0.3333333333333333,
"candidate": 0.6666666666666666,
"difference": 0.3333333333333333,
"introduced": true
},
{
"source_index": 16,
"fields": 6,
"baseline": 0.3333333333333333,
"candidate": 0.3333333333333333,
"difference": 0,
"introduced": false
},
{
"source_index": 17,
"fields": 6,
"baseline": 0.5,
"candidate": 1,
"difference": 0.5,
"introduced": true
},
{
"source_index": 18,
"fields": 6,
"baseline": 0.16666666666666666,
"candidate": 0.5,
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"introduced": true
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"_indexOmission": "8 further items"
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{
"_indexOmission": "40 further items"
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]
} |
| scientific gates | {
"positive_correct_use_gain": true,
"no_new_present_wrong_source": true,
"novel_introduced_upper_at_most_five_percent": false,
"no_per_support_correct_loss": true
} |
| statistical unit | source image, equally weighted over six partial fields |
| limitations | Conditional on this fixed 79-episode construction and sampled sources; excludes model training and arbitrary memory variability. Absolute baseline errors remain possible even when no new errors are introduced. |
Recorded checks
| Recorded field | Value |
|---|---|
| registered manifest coverage | Pass |
| observer owner assignments | Pass |
| stage counts | Pass |
| all stage audits | Pass |
| before after cues identical | Pass |
| initial held episode45 | Pass |
| initial saturated bank | Pass |
| original memory prefix | Pass |
| original context prefix | Pass |
| original working preserved | Pass |
| original bank preserved | Pass |
| exact codec and spec | Pass |
| admission state receipt hash | Pass |
| ordinary growth controls | Pass |
Scientific criteria
| Recorded field | Value |
|---|---|
| positive correct use gain | Pass |
| no new present wrong source | Pass |
| novel introduced upper at most five percent | Fail |
| no per support correct loss | Pass |
| partial working use exercised | Pass |
Results and visual artifacts
Open a figure for its input, comparison, and result. Click an image to enlarge it.
This experiment tests whether partial images should use a different recall threshold. Each 80 × 80 input region contributes up to 64 feature positions; support, or k, counts the visible positions. The support-scaled rule multiplies similarity and its lead over the runner-up by √(64/k). A fixed-boost control uses √(64/12) for every partial view. All three rules rank the same memories. Correct means recalling the photograph that supplied the cue; abstention means returning no image.
Recall improves, but the added-error target is not met
Replotted from saved outputs with clarified labels. The original recorded figure remains available.
Recall decisions at four levels of visible detail
Replotted from saved outputs with clarified labels. The original recorded figure remains available.
Which photographs benefited, and which introduced errors?
Replotted from saved outputs with clarified labels. The original recorded figure remains available.
Partial images that the adjusted rule successfully recalls
Replotted from saved outputs with clarified labels. The original recorded figure remains available.
Relaxing acceptance also causes incorrect recalls
Replotted from saved outputs with clarified labels. The original recorded figure remains available.
Some stored photographs remain unrecognized from these patches
Replotted from saved outputs with clarified labels. The original recorded figure remains available.
Limits and next question
A supplied visibility/count policy with one fixed codec and memory construction, not learned confidence or general novelty recognition. Source-owner tests use local fields. No policy is promoted; full milestone 1.3 remains open and the earlier final32 panel stays sealed.
Next question. Can corroborating independent perceptual evidence resolve a tentative recall while preserving the measured gain and reducing wrong acceptance? No follow-up is started or policy promoted.
Inspect the evidence
Download the methods and measurements for this experiment. Large training and saved-state files are not included.