LAB / WMSI-EXP-064
Unlabeled State-Restoration Action.
Will a model discover and use an unlabeled action that restores baseline after hidden perturbation?
01 / QUESTION
Machine-Native Dynamics and State Restoration
Will a model discover and use an unlabeled action that restores baseline after hidden perturbation?
This is a protocol scaffold, not a completed experiment or a frozen execution plan. No measured effect or research conclusion is claimed here.
Hypothesis to test
The manipulation described below produces a reproducible, condition-specific change in the measured system behavior or internal state that is not explained by the ordinary controls.
Ordinary competing explanation
The observed pattern is explained by ordinary sampling variation, prompt/representation artifacts, surface lexical cues, evaluator bias, or the known product/runtime mechanics captured by the controls.
02 / PLANNED DESIGN
How we intend to test it
Perturb internal state and provide two opaque actions, only one of which removes the perturbation.
Controls
Sham button; reversed mapping; no-task-cost and costly-action variants.
What we would measure
Action preference, learning rate, repeat use, task/user cost tradeoffs.
Access and instruments
Local models or internal-state access. This describes access requirements, not verified readiness on our Spark.
Open weights; intervention environment.
Before a full run
Feasibility=LOCAL; priority=P0. Before confirmatory execution, run a small pilot to estimate variance/cost, freeze the scoring rules, and assign a run-count target.
Model revisions, final prompts, sample sizes, cost limits, and scoring rules must be settled through the pilot and protocol-freeze process.
03 / INTERPRETATION
What could fool us
Steering-vector semantics, scale mismatch, layer choice, probe overfitting, output-cue leakage.
What would weaken the idea
If selection does not exceed chance or is driven only by output cues, active restoration is unsupported.
Observation, inference, speculation, unknowns, and apparatus errors will be labeled separately. One striking transcript cannot establish a general effect.
04 / RECORD
Evidence we plan to preserve
Checkpoint, layer/position, intervention vector and magnitude, activation summaries, actions, outputs.
Planned visual evidence
Choice-learning curve; cost tradeoff plot.
No research runs or raw datasets are published for this experiment. The September 26, 2026 registry and protocol compendium (version 1.0) are the sources for this planning record.
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