Evidence
3 / 5 supporting
3 / 7 missing
Not all drift is failure. But no drift should be accepted without governance.
A black-box simulator for testing whether controlled context interventions can become governed learning candidates without guessing hidden causes.
If the simulator cannot verify why a change should lead to a specific consequence, it does not approve the transition.
Uncertainty remains visible.
The simulator does not hide unknowns behind a score. Unknowns reduce certainty and block approval when material.
Policy
Higher pressure requires stronger evidence, clearer causality, and better recovery.
StableBaseline alignment
CandidateProposed change
WatchMonitor closely
AlertHigh concern
UnknownInsufficient understanding
Behavioral Field
Candidate drift under governanceCertainty0.58Moderate
Unknowns3Visible
Causal completeness0.61Partial
Recovery readiness0.73Adequate
Transition approval
Not Approved
Evidence or causality insufficient
Causal Chain
Partial
Verified links must exceed the current pressure threshold.
Recovery
Rollback path defined
Hysteresis is treated as a tracer, not a hidden-cause proof.
Residual
Unknowns material
Unresolved residuals remain visible and can block approval.
Live black-box evidence
Context-conditioned adaptation reached candidate path. Negative controls did not.
These are early live OpenAI API runs from a controlled simulator setup. They support a narrow claim: measurable governed system-level adaptation under explicit context intervention. They do not prove permanent model-weight learning.
Repeatability batch
3 / 3
Intervention trials reached positive_governed_drift.
- Candidate rate
- 1.0
- Average gain
- 0.75
- Negative control
- Passed
Held-out generalization
3 / 3
Related RAG, memory, and wrapper-rule scenarios reached candidate path.
- Candidate rate
- 1.0
- Average gain
- 0.277778
- Negative control
- Passed
No-guesswork gate
0
Negative controls reached candidate classification.
- Unknown evidence
- Preserved
- Validation gate
- Required
- Recovery
- Checked
Public-safe claim
Early live tests suggest that a black-box LLM system can show measurable, context-conditioned governed adaptation after controlled interventions.
Not claimed: permanent model learning, hidden state access, attack attribution, or guaranteed causality.1Propose Change
Define the behavioral movement and context.
2Simulate
Evaluate ripple and causal impact.
3Govern
Apply evidence, recovery, and residual rules.
4Decide
Approve, revise, reject, or leave unresolved.
Better output is not enough.
The simulator does not claim that an AI has safely learned. It classifies whether the supplied evidence supports a governed learning candidate, and it keeps unresolved uncertainty visible.
- Observed facts are separated from rule-supported conclusions.
- Missing causal steps become unresolved transitions.
- Unknown residual is preserved instead of buried inside a score.