Learning and Adaptive Testing of Nondeterministic State Machines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 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 it