On the average complexity of partial derivative transducers
Bibliographic record
Abstract
2D regular expressions represent rational relations over two alphabets Σ and Δ. In standard 2D expressions (S2D-RE) the basic terms are generators of Σ⋆×Δ⋆, while in generalised 2D expressions (2D-RE) the basic terms are pairs of (ordinary) regular expressions over one alphabet (1D). In this paper we study the average state complexity of partial derivative standard transducers (TPD) for both S2D-RE and 2D-RE. For S2D-RE we obtain the same asymptotic bounds as for partial derivative automata. For 2D-RE, while in the worst case the number of states of TPD can be O(n2), where n is the size of the expression, asymptotically and on average that value is bounded from above by O(n32). We also show that asymptotically and on average the alphabetic size of a 2D-RE is half of its size. All results are obtained in the framework of analytic combinatorics considering generating functions of parametrised combinatorial classes defined implicitly by algebraic curves. In particular, we generalise the methods developed in previous work to a broad class of analytic functions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 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".