Exploiting Known Structures to Approximate Normal Cones
Why this work is in the frame
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Bibliographic record
Abstract
The normal cone to a constraint set plays a key role in optimization theory, algorithms, and applications. We consider the question of how to approximate the normal cone to a set under the assumption that the set is provided through an oracle function or collection of oracle functions, but contains some exploitable structure. We provide a new simplex gradient-based approximation technique that works for sets defined through a finite number of oracle-based functions. We further present novel results showing that, under a non-degeneracy condition, approximating normal cones to intersections of sets is possible by taking sums of approximations. Finally, we provide numerical results that exemplify the accuracy of the simplex gradient approximation when it is applicable, and the failure of this technique when a linear independence constraint qualification is not met.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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