On the completeness of compositional reasoning methods
Bibliographic record
Abstract
Hardware systems and reactive software systems can be described as the composition of several concurrently active processes. Automated reasoning based on model checking algorithms can substantially increase confidence in the overall reliability of a system. Direct methods for model checking a concurrent composition, however, usually suffer from the explosion in the number of program states that arises from concurrency. Reasoning compositionally about individual processes helps mitigate this problem. A number of rules have been proposed for compositional reasoning, typically based on an assume-guarantee reasoning paradigm. Reasoning with these rules can be delicate, as some are syntactically circular in nature, in that assumptions and guarantees are mutually dependent. This is known to be a source of unsoundness. In this article, we investigate rules for compositional reasoning from the viewpoint of completeness . We show that several rules are incomplete: that is, there are properties whose validity cannot be established using (only) these rules. We derive a new, circular, reasoning rule and show it to be sound and complete. We show that the auxiliary assertions needed for completeness need be defined only on the interface of the component processes. We also show that the two main paradigms of circular and noncircular reasoning are closely related, in that a proof of one type can be transformed in a straightforward manner to one of the other type. These results give some insight into the applicability of compositional reasoning methods.
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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 | 0.037 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".