Automata Recognizing No Words: A Statistical Approach
Bibliographic record
Abstract
How likely is that a randomly given (non-) deterministic finite automaton recognizes no word? A quick reflection seems to indicate that not too many finite automata accept no word; but, can this intuition be confirmed? In this paper we offer a statistical approach which allows us to conclude that for automata, with a large enough number of states, the probability that a given (non-) deterministic finite automaton recognizes no word is close to zero. More precisely, we will show, with a high degree of accuracy (i.e., with precision higher than 99 % and level of confidence 0.9973), that for both deterministic and non-deterministic finite automata: a) the probability that an automaton recognizes no word tends to zero when the number of states and the number of letters in the alphabet tend to infinity, b) if the number of states is fixed and rather small, then even if the number of letters of the alphabet of the automaton tends to infinity, the probability is strictly positive. The result a) is obtained via a statistical analysis; for b) we use a combinatorial and statistical analysis. The present analysis shows that for all practical purposes the fraction of automata recognizing
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".