Stratified Commutativity in Verification Algorithms for Concurrent Programs
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Bibliographic record
Abstract
The importance of exploiting commutativity relations in verification algorithms for concurrent programs is well-known. They can help simplify the proof and improve the time and space efficiency. This paper studies commutativity relations as a first-class object in the setting of verification algorithms for concurrent programs. A first contribution is a general framework for abstract commutativity relations . We introduce a general soundness condition for commutativity relations, and present a method to automatically derive sound abstract commutativity relations from a given proof. The method can be used in a verification algorithm based on abstraction refinement to compute a new commutativity relation in each iteration of the abstraction refinement loop. A second result is a general proof rule that allows one to combine multiple commutativity relations, with incomparable power, in a stratified way that preserves soundness and allows one to profit from the full power of the combined relations. We present an algorithm for the stratified proof rule that performs an optimal combination (in a sense made formal), enabling usage of stratified commutativity in algorithmic verification. We empirically evaluate the impact of abstract commutativity and stratified combination of commutativity relations on verification algorithms for concurrent programs.
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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 | 0.001 |
| 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 | 0.002 | 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