Falsified Model-Invariant Safety-Preserving Control With Application to Closed-Loop Anesthesia
Why this work is in the frame
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Bibliographic record
Abstract
This brief introduces a novel safety-preserving control scheme with minimal conservatism for uncertain systems. We have recently introduced the model-invariant safety verification technique that provides a formal guarantee of safety for systems with multiplicative model uncertainty. This approach requires a multi-model description of model uncertainty. The resulting safety system may be conservative for systems that do not exhibit the worst case dynamical response. In this brief, we employ model falsification to reduce conservatism of the model-invariant safety verification technique. Members of a model set that characterizes model uncertainty are falsified if discrepancy between predictions of those models and measured responses of the uncertain system is established, thereby reducing model uncertainty. To demonstrate the effectiveness of the proposed technique, we formalize a model-invariant safety system for closed-loop propofol anesthesia. The safety system maintains predicted propofol concentration in plasma as well as the patient's blood pressure within safety bounds despite uncertainty in patient responses to propofol.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it