Bibliographic record
Abstract
Let $ES(n)$ be the minimal integer such that any set of $ES(n)$ points in the\nplane in general position contains $n$ points in convex position. The problem\nof estimating $ES(n)$ was first formulated by Erd\\H{o}s and Szekeres, who\nproved that $ES(n) \\leq \\binom{2n-4}{n-2}+1$. The current best upper bound,\n$\\lim\\sup_{n \\to \\infty} \\frac{ES(n)}{\\binom{2n-5}{n-2}}\\le \\frac{29}{32}$, is\ndue to Vlachos. We improve this to $$\\lim\\sup_{n \\to \\infty}\n\\frac{ES(n)}{\\binom{2n-5}{n-2}}\\le \\frac{7}{8}.$$\n
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".