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 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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".