A More Efficient Time Petri Net State Space Abstraction Useful to Model Checking Timed Linear Properties
Why this work is in the frame
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Bibliographic record
Abstract
We consider here time Petri nets (TPN model). We first propose an abstraction to its generally infinite state space which preserves linear properties of the TPN model. Comparing with TPN abstractions proposed in the literature, our abstraction produces graphs which are both smaller and faster to compute. In addition, our characterization of agglomerated states allows a significant gain in space. Afterwards, we show how to apply Yoneda's partial order reduction technique to construct directly reduced graphs useful to verify LTL$_{-X}$ properties of the model. Using our approach, both time and space complexities are reduced. Finally, we propose a time extension for Buchi automata which is useful to model checking timed linear properties of the model, using the abstraction proposed here.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it