VaqUoT: A Tool for Vacuity Detection
Bibliographic record
Abstract
Abstract. This paper presents VaqUoT – a University of Toronto tool for vacuity detection, built on top of NuSMV. In one model-checking pass, VaqUoT establishes the truth value of a CTL formula as well as the largest set of nonoverlapping subformulas in which that formula is vacuous. We describe the tool and evaluate its performance. During model-checking, properties are sometimes satisfied by models for the wrong reasons. Suppose a CTL formula ψ = AG (r ∨ y ∨ g) is checked against a model of a traffic-light controller, where atomic propositions r, y, and g stand for the colors of the light: red, yellow, and green, respectively. The formula is intended to express that in every state the light has one of these colors. This requirement may not be satisfied, even if ψ passes the check: it is possible for the model to be overconstrained so that the light always stays red. In such cases, an answer “true”, given usually by modelcheckers, is insufficient; a user needs to know why the formula is satisfied. Vacuity detection [2] can help, by determining whether some parts of the formula do not matter for the verification, i.e., are vacuous. For instance, y and g should be reported as vacuous
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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 | 0.000 | 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 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".