A New Branch and Bound Method for Incremental Satisfiability Problem.
Bibliographic record
Abstract
We present in this paper a new method based on branch and bound for solving the incremental satisfiability (SAT) problem. More precisely, the goal of the method is to maintain, in an incremental manner, the satisfiability of a given boolean formula in Conjunctive Normal Form (CNF) anytime a new set of clauses is added. Solving incremental SAT is very appealing for a wide variety of real-life combinatorial applications such as online scheduling and planning, robot motion planning, network routing and transportation scheduling. We will show that the branch and bound algorithm can be improved by taking advantage of the structure of the CNF formula. Experimental study, on randomly generated SAT instances taken from the well known SAT library, demonstrates the efficiency of our method especially for large SAT problems. KeywordsBoolean Satisfiability, Branch and Bound, Local Search.
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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".