Bibliographic record
Abstract
For over a decade, researchers in formal methods tried to create formalisms that permit natural specification of systems and allow mathematical reasoning about their correctness. The availability of fully-automated reasoning tools enables more non-specialists to use formal methods effectively-their responsibility reduces to just specifying the model and expressing the desired properties. Thus, it is essential that these properties be represented in a language that is easy to use and sufficiently expressive. Linear-time temporal logic is a formalism that has been extensively used by researchers for specifying properties of systems. When such properties are closed under stuttering, i.e. their interpretation is not modified by transitions that leave the system in the same state, verification tools can utilize a partial-order reduction technique to reduce the size of the model and thus analyze larger systems. If LTL formulas do not contain the "next" operator, the formulas are closed under stuttering, but the resulting language is not expressive enough to capture many important properties, e.g., properties involving events. Determining if an arbitrary LTL formula is closed under stuttering is hard-it has been proven to be PSPACE-complete. We relax the restriction on LTL that guarantees closure under stuttering, introduce the notion of edges in the context of LTL, and provide theorems that enable syntactic reasoning about closure under stuttering of LTL formulas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".