Learning and Adaptive Testing of Nondeterministic State Machines
Bibliographic record
Abstract
The paper addresses the problems of active learning and conformance testing of systems modeled by nondeterministic Mealy machines (NFSM). It presents a unified SAT-based approach originally proposed by the authors for deterministic FSMs and now generalized to partial nondeterministic machines and checking experiments. Learning a nondeterministic black box, the approach neither needs a Teacher nor uses it a conformance tester to approximate equivalence queries. The idea behind this approach is to infer from a current set of traces not one, but two inequivalent conjectures, use an input sequence distinguishing them in an output query, and update the current trace set with an observed trace to obtain a new pair of distinguishable conjectures, if possible. The classical active learning problem is further generalized by adding a nondeterministic specification FSM, which defines the solution space. The setup unifies the learning and adaptive testing problems and makes them equisolvable with the proposed approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".