Leveraging dominators for preprocessing QBF
Bibliographic record
Abstract
Many CAD for VLSI problems can be naturally encoded as Quantified Boolean Formulas (QBFs) and solved with QBF solvers. Furthermore, such problems often contain circuit-based information that is lost during the translation to Conjunctive Normal Form (CNF), the format accepted by most modern solvers. In this work, a novel preprocessing framework for circuit-based QBF problems is presented. It leverages structural circuit dominators to reduce the problem size and expedite the solving process. Our circuit-based QBF preprocessor PReDom recursively reduces dominated subcircuits to return a simpler but equisatisfiable QBF instance. A rigorous proof is given for eliminating subcircuits dominated by single outputs, irrespective of input quantifiers. Experimental results are presented for circuit diameter computation problems. With preprocessing times of at most five seconds using PReDom, three state-of-the-art QBF solvers can solve 27% to 45% of our problem instances, compared to none without preprocessing.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".