Chasing Minimal Inductive Validity Cores in Hardware Model Checking
Bibliographic record
Abstract
Model checking of safety properties is fundamental in formal verification. When a safety property is found to hold, the model checker provides (at best) a machine-checkable certificate that gives limited insight to users and little confidence that the check passes for the “right” reasons, rather than due to e.g., vacuity or unjustified assumptions. Recently, inductive validity cores (IVCs) have been developed to address this issue. In this paper, we lift several algorithms from the field of UNSAT core extraction in order to compute minimal IVCs of hardware safety checking problems. The MARCO algorithm extracts all minimal cores of an UNSAT formula by efficiently exploring the formula's power set, and has already been applied to compute IVCs in software safety checking. The CAMUS algorithm for UNSAT core extraction exploits a duality between minimal correction subsets (MCSes) of a formula and minimal UNSAT cores. We adapt the algorithms to the hardware IVC context, construct a hybrid algorithm that subsumes both CAMUS and MARCO, and introduce novel domain-specific optimizations. Several instances of the hybrid algorithm are presented (including CAMUS and MARCO themselves, among other novel variants) and evaluated empirically on hardware model checking competition circuits, demonstrating the practicality of the proposed algorithm.
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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 | 0.000 |
| 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 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".