Random search of AND-OR graphs representing finite-state models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Model checking tools have been effective in testing concurrent software represented by communicating finite-state machines. But these tools may require a very large amount of memory. A finite-state model can be translated automatically into a compact AND-OR graph. We use an abductive random search scheme to extract, from the AND-OR graph, information about the execution of the program represented by the original finite-state model.;We use the search to measure testability. For AND-OR graphs representing highly testable programs, we find quickly everything it is possible to find; that is, if the number of unique goals found is plotted, we see a quick rise to a level plateau. The search can also be used to prove simple logical properties.;To determine what makes a finite-state model more or less testable, we analyze random search results for 15,000 randomly generated models with a range of attributes. We also show how this technique can be used on a model much too large for model checking tools.
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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 | 0.000 | 0.000 |
| 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 it