Bibliographic record
Abstract
There is a growing interest in techniques for detecting whether a logic specification is satisfied too easily, or vacuously . For example, the specification “every request is eventually followed by an acknowledgment” is satisfied vacuously by a system that never generates any requests. Vacuous satisfaction misleads users of model-checking into thinking that a system is correct. It is a serious problem in practice. There are several existing definitions of vacuity. Originally, Beer et al. [1997] formalized vacuity as insensitivity to syntactic perturbation ( syntactic vacuity ). This formulation captures the intuition of “vacuity” when applied to a single occurrence of a subformula. Armoni et al. argued that vacuity must be robust ; not affected by semantically invariant changes, such as extending a model with additional atomic propositions. They show that syntactic vacuity is not robust for subformulas of linear temporal logic, and propose an alternative definition; trace vacuity . In this article, we continue this line of research. We show that trace vacuity is not robust for branching time logic. We further refine the notion of vacuity so that it applies uniformly to linear and branching time logic and does not suffer from the common pitfalls of prior definitions. Our new definition, bisimulation vacuity , is a proper and nontrivial extension of both syntactic and trace vacuity. We discuss the complexity of detecting bisimulation vacuity, and identify several practically-relevant subsets of CTL* for which vacuity detection problem is reducible to model-checking. We believe that in most practical applications, bisimulation vacuity provides both the desired theoretical properties and is tractable computationally.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".