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Cliste experiment
Cleanup diagnostic: numerical decision verification
Registered 9 Oct 2026 · Snapshot 12 Oct 2026
The question
Check whether all saved cleanup and recall decisions agree with independent float64 arithmetic while preserving run 001's failed score tolerance.
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
Recompute every saved prototype decision and all ordinary/counterfactual recall decisions in float64; preserve the parent failure and its exact tolerance.
What happened
Failed before numerical computation: the isolation wrapper omitted the /dev mount and PyTorch could not open /dev/urandom. This is an execution-binding failure. The identical numerical method is separately registered as run003 with the established device and process mounts restored; this failed attempt remains preserved.
Results and visual artifacts
Open a figure for its input, comparison, and result. Click an image to enlarge it.
This verification attempt has no figure. Its saved numerical checks and failed outcome remain in the methods and results records.
No visual artifacts were available at snapshot time.
Limits and next question
Post-result verification of existing evidence, not new inference or independent replication. Original run 001 remains failed. No threshold, cleanup policy, acquired state or final-panel exposure changes; full milestone 1.3 remains open.
Next question. Inspect the separately registered run003 execution-binding recovery; no numerical threshold or scientific method changed.
Inspect the evidence
Download the methods and measurements for this experiment. Large training and saved-state files are not included.