Novel tree-search method for synthesizing SMT strategies
Bibliographic record
Abstract
Abstract Modern SMT solvers, such as Z3, allow solver users to customize strategies to improve performance on their specific use cases. However, handcrafting an optimized strategy for a specific class of SMT instances remains a complex and demanding task for both solver developers and users alike. In this paper, we address the problem of automated SMT strategy synthesis via a novel method based on Monte-Carlo Tree Search (MCTS). We formulate strategy synthesis as a sequential decision-making process, where the search tree corresponds to the strategy space. Subsequently, we employ MCTS to navigate this vast search space. Compared to the conventional MCTS, we introduce two heuristics—layered and staged search—that enable our method to identify effective strategies with lower costs. We implement our method, dubbed Z3alpha, upon the Z3 SMT solver. Our experiments demonstrate that Z3alpha outperforms the default Z3 solver and the state-of-the-art synthesis tool Fastsmt on the majority of the evaluated benchmark sets, while producing more interpretable strategies than FastSMT. At SMT-COMP’24, among the 16 participating logics, Z3alpha improved upon the default Z3 in 12 cases and helped solve hundreds more instances in QF_NIA and QF_NRA, winning their respective divisions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".