Bibliographic record
Abstract
The optimal control of stochastic nonlinear systems is difficult in general, and introducing risk-awareness only compounds the difficulty. Motivated by this, we propose a risk-aware, suboptimal controller design applicable to a general class of stochastic systems with both additive and multiplicative disturbances. Moreover, the proposed controller is computationally tractable, admitting a Riccati-like form. To develop this controller, we build upon earlier work to develop a theory of cone-bounded functions between Banach and Hilbert spaces that allows for more precise cone-bounds. This enables us to then extend this work to a novel class of quasi-cone-bounded systems. By deriving key properties of such systems, we are able to derive a suboptimal controller with guaranteed regulation upper bounds. Finally, we present a reformulation of variance suppression allowing us to extend that notion of risk-awareness to nonlinear systems, and therefore to (quasi-)cone-bounded suboptimal controllers.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".