Bibliographic record
Abstract
In this work, we propose a bounded verification approach for state machine (SM) models that is independent of any model checking tools. This independence is achieved by encoding the execution semantics of SM models as Satisfiability Modulo Theories (SMT) formulas that reduce the verification of a SM to the satisfiability problem for its corresponding formula. More specifically, our approach takes as input a SM model, a depth bound, and the system properties (as invariants), and then automatically verifies models of systems in a three-phase process: (1) First it generates all possible execution paths of the model to the specified bound, and encodes each of the execution paths as SMT formulas; (2) It then augments the SMT formulas with the negation of the given invariants; and (3) Finally, it uses an SMT solver to check the satisfiability of the instrumented formula. We have applied our approach in the context of UML-RT (the UML profile for modeling real-time embedded systems) and assessed the applicability, performance, and scalability of our approach using several case studies.
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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 | 0.001 |
| Open science | 0.001 | 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 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".