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Cliste experiment

Remembered continuation from partial visual perception

Execution: completedScientific verdict: bounded loop demonstrated quality provisional

The question

Connect partial VSA recall to retained temporal context and actual working continuation across new experience, growing memory and restart; compare baseline and support-scaled acceptance with visible errors.

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

Recovery of interrupted run001. Completed before/middle, ordinary acquisition and integrated continuity are inherited byte for byte. Only the incomplete after panel and remaining controls are executed, then all six panels are independently audited under the original criteria. Inputs are already exposed.

What happened

Completed recovery of the interrupted comparison: 32 newly admitted photographs yield 25/128 baseline versus 60/128 support-scaled correct partial-cue paths, each requiring the intended anchor and both successors (+27.34 percentage points; source-bootstrap 95% interval 19.53–35.16). Full local fields give 48/64 under both policies. Six numerical/causal panels and the integrated hold/save/new-intake/resume proof pass. Earlier-photo recall loses successes as competition grows; video partial paths are 1/32 versus 4/32. All final novel fields reject, but earlier full-field false accepts remain. Bounded 1.6 is demonstrated; quality is provisional.

Measurements
Measurements — values preserved from this run
Recorded fieldValue
primary
{
  "correct": {
    "mean": 0.2734375,
    "ci95": [
      0.1953125,
      0.3515625
    ],
    "sources": 32,
    "samples": 10000,
    "seed": 1606
  },
  "correct_two": {
    "mean": 0.2734375,
    "ci95": [
      0.1953125,
      0.3515625
    ],
    "sources": 32,
    "samples": 10000,
    "seed": 1606
  },
  "wrong": {
    "mean": 0,
    "ci95": [
      0,
      0
    ],
    "sources": 32,
    "samples": 10000,
    "seed": 1606
  },
  "missed": {
    "mean": -0.2734375,
    "ci95": [
      -0.3515625,
      -0.1953125
    ],
    "sources": 32,
    "samples": 10000,
    "seed": 1606
  }
}
matched changes
{
  "added_correct": 35,
  "lost_correct": 0,
  "added_wrong": 0,
  "lost_wrong": 0
}
never admitted
{
  "baseline": {
    "fields": 128,
    "sources": 32,
    "anchor_correct": 0,
    "anchor_wrong": 0,
    "anchor_abstained": 128,
    "correct": 0,
    "correct_two": 0,
    "wrong": 0,
    "missed": 128,
    "endpoint_only": 0,
    "temporal_wrong_after_correct_anchor": 0,
    "stopped_after_correct_anchor": 0,
    "sources_with_wrong": 0,
    "stop_reasons": {
      "cue_rejected": 128
    }
  },
  "support_scaled": {
    "fields": 128,
    "sources": 32,
    "anchor_correct": 0,
    "anchor_wrong": 0,
    "anchor_abstained": 128,
    "correct": 0,
    "correct_two": 0,
    "wrong": 0,
    "missed": 128,
    "endpoint_only": 0,
    "temporal_wrong_after_correct_anchor": 0,
    "stopped_after_correct_anchor": 0,
    "sources_with_wrong": 0,
    "stop_reasons": {
      "cue_rejected": 128
    }
  },
  "paired_wrong_difference": {
    "mean": 0,
    "ci95": [
      0,
      0
    ],
    "sources": 32,
    "samples": 10000,
    "seed": 1606
  }
}
strata
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    "anchor_wrong": 0,
    "anchor_abstained": 8,
    "correct": 0,
    "correct_two": 0,
    "wrong": 0,
    "missed": 8,
    "endpoint_only": 0,
    "temporal_wrong_after_correct_anchor": 0,
    "stopped_after_correct_anchor": 0,
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    "stop_reasons": {
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    "missed": 5,
    "endpoint_only": 0,
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    "stopped_after_correct_anchor": 0,
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    "stop_reasons": {
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      "cue_rejected": 5
    }
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  },
  {
    "_indexOmission": "144 further items"
  }
]
usability floor
[
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    "endpoint_only": 0,
    "temporal_wrong_after_correct_anchor": 0,
    "stopped_after_correct_anchor": 0,
    "sources_with_wrong": 0,
    "stop_reasons": {
      "completed": 60,
      "cue_rejected": 68
    },
    "wrong_dominates": false
  },
  {
    "stage": "after",
    "arm": "support_scaled",
    "support": "full",
    "fields": 64,
    "sources": 32,
    "anchor_correct": 48,
    "anchor_wrong": 0,
    "anchor_abstained": 16,
    "correct": 48,
    "correct_two": 48,
    "wrong": 0,
    "missed": 16,
    "endpoint_only": 0,
    "temporal_wrong_after_correct_anchor": 0,
    "stopped_after_correct_anchor": 0,
    "sources_with_wrong": 0,
    "stop_reasons": {
      "completed": 48,
      "cue_rejected": 16
    },
    "wrong_dominates": false
  }
]
per source
[
  {
    "stage": "after",
    "source_key": "earlier:0",
    "arm": "baseline",
    "support": "full",
    "fields": 2,
    "sources": 1,
    "anchor_correct": 2,
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    "anchor_abstained": 0,
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    "correct_two": 2,
    "wrong": 0,
    "missed": 0,
    "endpoint_only": 0,
    "temporal_wrong_after_correct_anchor": 0,
    "stopped_after_correct_anchor": 0,
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    "stop_reasons": {
      "completed": 2
    }
  },
  {
    "stage": "after",
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    "fields": 4,
    "sources": 1,
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    "anchor_wrong": 0,
    "anchor_abstained": 3,
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    "stopped_after_correct_anchor": 0,
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    "stop_reasons": {
      "cue_rejected": 3,
      "completed": 1
    }
  },
  {
    "stage": "after",
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    "anchor_abstained": 0,
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    "correct_two": 2,
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    "endpoint_only": 0,
    "temporal_wrong_after_correct_anchor": 0,
    "stopped_after_correct_anchor": 0,
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    "stop_reasons": {
      "completed": 2
    }
  },
  {
    "stage": "after",
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    "fields": 4,
    "sources": 1,
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    "anchor_abstained": 1,
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      "completed": 3,
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    }
  },
  {
    "stage": "after",
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    "stop_reasons": {
      "cue_rejected": 2
    }
  },
  {
    "stage": "after",
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      "cue_rejected": 4
    }
  },
  {
    "stage": "after",
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      "cue_rejected": 2
    }
  },
  {
    "stage": "after",
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    }
  },
  {
    "stage": "after",
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    }
  },
  {
    "stage": "after",
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    }
  },
  {
    "stage": "after",
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  },
  {
    "stage": "after",
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    }
  },
  {
    "stage": "after",
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    }
  },
  {
    "stage": "after",
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      "completed": 2
    }
  },
  {
    "stage": "after",
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    }
  },
  {
    "stage": "after",
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    }
  },
  {
    "stage": "after",
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  },
  {
    "stage": "after",
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    "stop_reasons": {
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    }
  },
  {
    "stage": "after",
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    }
  },
  {
    "stage": "after",
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    "stop_reasons": {
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      "completed": 1
    }
  },
  {
    "stage": "after",
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    "stop_reasons": {
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    }
  },
  {
    "stage": "after",
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    }
  },
  {
    "stage": "after",
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    }
  },
  {
    "stage": "after",
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    "stop_reasons": {
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    }
  },
  {
    "_indexOmission": "888 further items"
  }
]
paired cohorts
[
  {
    "stage": "after",
    "cohort": "first",
    "present": true,
    "support": "full",
    "correct": {
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      "ci95": [
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      ],
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      "samples": 10000,
      "seed": 1606
    },
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        0
      ],
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    },
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      ],
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    },
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      ],
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    }
  },
  {
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      ],
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    },
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    },
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    },
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    }
  },
  {
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    },
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    },
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    },
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      ],
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    }
  },
  {
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    },
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      ],
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    }
  },
  {
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  },
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    },
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    }
  },
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    },
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    }
  },
  {
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    },
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    },
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  {
    "_indexOmission": "146 further items"
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]
bootstrap
{
  "samples": 10000,
  "seed": 1606,
  "unit": "photographic source; all matched fields remain together"
}
video source videos2
limitationsFinite acquired content, supplied masks and replay direction; video frames remain correlated and restart/control rows are excluded from independent-sample counts.
integration evidence
{
  "context_intervention_exercised": true,
  "context_intervention_followed": true,
  "earlier_retention_observed": true,
  "restart_exact": true,
  "intake_between_replay_steps": true
}
Recorded checks
Recorded checks — values preserved from this run
Recorded fieldValue
before auditPass
before registered countPass
middle auditPass
middle registered countPass
after auditPass
after registered countPass
restart auditPass
restart registered countPass
permuted auditPass
permuted registered countPass
context swap auditPass
context swap registered countPass
restart operative equivalencePass
permuted operative equivalencePass

Results and visual artifacts

Open a figure for its input, comparison, and result. Click an image to enlarge it.

A cue is an 80 × 80 region taken from a photograph. Gray pixels are hidden; the visible region supplies 12, 24, or all 64 local feature positions. The baseline rule uses fixed similarity thresholds; the support-scaled rule adjusts the scores for how much is visible. After recalling an image, Cliste follows stored temporal context for two more encounters. These are memories of presentation order, not predictions of events in the pictured world.

More partial cues recover the complete remembered sequence
More partial cues recover the complete remembered sequence
After memory grew to 127 encounters, the adjusted rule recovered the correct image and both subsequent encounters for 60 of 128 partial cues, versus 25 for the baseline. The left panel tests 32 stored photographs; the right tests 32 never stored. Each photograph supplies four related cues: two regions, each tested with 12 and 24 visible feature positions. Both rules rejected all 128 unfamiliar cues in this final panel; earlier false responses are shown separately below.

Original recorded figure ↗

A successful recall and a cue that remains ambiguous
A successful recall and a cue that remains ambiguous
Top: a small patch of a bus retrieves the bus photograph, then the elephant and donut photographs that were encountered after it. The baseline returned no sequence. Bottom: the donut patch leaves competing memories too close to distinguish, so no sequence is returned. Column two contains original targets for reader comparison; the three output columns are decoded from actual stored working vectors. These two examples illustrate behavior, not its frequency.

Original recorded figure ↗

More visible detail helps one region, but not another
More visible detail helps one region, but not another
Each row shows progressively more of one 80 × 80 region of the donut photograph: 12, 24, then 64 usable feature positions. In the top row, the fully visible region retrieves the correct image and both next encounters. The second region still produces no sequence. Panels show inputs, with the resulting decision above each; the reveal schedule was fixed in advance.

Original recorded figure ↗

The original image sequences behind those examples
The original image sequences behind those examples
The top row is the bus → elephant → donut sequence recalled in the successful example. The bottom row is the donut → traffic lights → clock sequence sought by the unresolved cue. These are original photographs shown for comparison, not model outputs. The recall process had access to stored vectors, not these source files.

Original recorded figure ↗

An unfamiliar image triggers a wrong remembered sequence
An unfamiliar image triggers a wrong remembered sequence
At the earlier stage with 79 stored encounters, this cue came from a photograph that was never stored. The baseline nevertheless returned the room, window, and airplane memories. Those are real decoded memory outputs, but they are the wrong response to the cue. This was first published as a preliminary accepted response; the completed audit identifies it as a false recall.

Original recorded figure ↗

Limits and next question

Supplied 80×80 local visibility and two-step control, one continuing life; the full-field condition is not the full photograph. Recovery reuses exposed inputs. Earlier recall regressions, earlier novel-source false accepts, and weak correlated video remain visible. Remembered order is not prediction or learned attention. Prior confidence criterion remains failed; baseline default unchanged; full 1.3 and final sealed panel remain open.

Next question. The completed optimization review proposes one profiler-confirmed bounded transient region/norm-cache comparison, with exact behavior guards and explicit RAM/VRAM tradeoff; then return to useful acquired cleanup, vocabulary growth and changed-view transfer. No optimization experiment has been run.

Inspect the evidence

Download the methods and measurements for this experiment. Large training and saved-state files are not included.