Near Optimal LQR Performance for a Compact Set of Plants
Why this work is in the frame
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Bibliographic record
Abstract
Here we consider the problem of providing near optimal performance (in this context, "near optimal performance" means performance as close to optimality as desired) for a large set of possible models. We adopt the linear quadratic regulator (LQR) framework in the single-input-single-output (SISO) setting, and prove that given a compact set of controllable and observable plant models of a fixed order, we can construct a single linear periodic controller (LPC) which provides near optimal LQR performance. Since the controller is linear, it automatically has the nice feature that there is some degree of tolerance to unmodeled dynamics. The approach is also shown to work if the goal is the more modest one of pole placement, and it can be simplified if there is additional structure to the plant model
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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