Bibliographic record
Abstract
Partial-order reduction methods form a collection of state exploration techniques set to relieve the stateexplosion problem in concurrent program verification. One such method is implemented in the verification tool SPIN. Its use often reduces significantly the memory and time needed for verifying local and termination properties of concurrent programs and, moreover, for verifying that concurrent programs satisfy their linear temporal logic specifications (i.e. for LTL model-checking). This paper builds on SPIN's partial-order reduction method to yield an approach which enables further reductions in space and time for verifying concurrent programs. Keywords Concurrency, program correctness, model-checking, partial-order reduction, temporal logic 1 Introduction Partial-order reduction methods [2-4, 7, 10, 19, 20, 24-26] form a collection of state exploration techniques set to relieve the state-explosion problem in concurrent program verification. The main observation underlyin...
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".