A Propositional Proof System with Quantification Over Permutations
Bibliographic record
Abstract
We introduce a new propositional proof system, which we call H, that allows quantification over permutations. In H we may write (∃ab)α and (∀ab)α, which is semantically equivalent to α(a,b)∨α(b,a) and α(a,b)∧ α(b,a), respectively. We show that H with cuts restricted to Σ _1 formulas (we denote this system H _1 ) simulates efficiently the Hajós calculus (HC) for constructing graphs which are non-3-colorable. This shows that short proofs over formulas that assert the existence of permutations can capture polynomial time reasoning (as by [9], HC is equivalent in strength to EF, which in turn captures polytime reasoning). We also show that EF simulates efficiently H ^*_1 , which is H _1 with proofs restricted to being tree-like. In short, we show that H ^*_1 ⩽ _p EF⩽ _p H _1 .
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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 | 0.000 |
| 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 | 0.001 |
| 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 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".