LINEAR-TIME PRIME DECOMPOSITION OF REGULAR PREFIX CODES
Why this work is in the frame
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Bibliographic record
Abstract
One of the new approaches to data classification uses prefix codes and finite state automata as representations of prefix codes. A prefix code is a (possibly infinite) set of strings such that no string is a prefix of another one. An important task driven by the need for the efficient storage of such automata in memory is the decomposition (in the sense of formal languages concatenation) of prefix codes into prime factors. We investigate properties of such prefix code decompositions. A prime decomposition is a decomposition of a prefix code into a concatenation of nontrivial prime prefix codes. A prefix code is prime if it cannot be decomposed into at least two nontrivial prefix codes. In the paper a linear time algorithm is designed which finds the prime decomposition F 1 F 2 …F k of a regular prefix code F given by its minimal deterministic automaton. Our results are especially interesting for infinite regular prefix codes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 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 it