Using inclusion abstraction to construct Atomic State Class Graphs for Time Petri Nets
Why this work is in the frame
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Bibliographic record
Abstract
We show in this paper how to contract the TPN state space into a graph that captures all its CTL* properties. This graph, called Atomic State Class Graph (ASCG), is finite if and only if, the model is bounded. To achieve this objective, we use a refinement technique similar to what is proposed in Berthomieu and Vernadat (2003) and Yoneda and Ryuba (1998). In such a technique, an intermediate contraction of the TPN state space is first built then refined until CTL* properties are restored. Compared with the approaches in Berthomieu and Vernadat (2003) and Yoneda and Ryuba (1998), we use inclusion abstraction during all phases of the construction process while reducing the complexity of computations. Our approach allows us to construct smaller ASCGs in shorter times (more than five times faster in certain cases).
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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