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Record W2556118493

Depth-based Local Search Heuristics for Boolean Circuit Satisfiability

2010· article· en· W2556118493 on OpenAlexaff
Anton Belov, Matti J„ärvisalo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicFormal Methods in Verification
Canadian institutionsYork University
Fundersnot available
KeywordsHeuristicsBoolean satisfiability problemHeuristicSatisfiabilityCompleteness (order theory)Local search (optimization)Maximum satisfiability problemComputer scienceAlgorithmTheoretical computer scienceVariable (mathematics)Mathematical optimizationMathematicsBoolean function
DOInot available

Abstract

fetched live from OpenAlex

We propose a structure-exploiting heuristic for the justification-based stochastic local search (SLS) method CRSat for Boolean circuit satisfiability. Experimental evaluation shows that the proposed depth-based heuristic significantly improved the performance of CRSat on structural instances arising from industrial applications. A proof of probabilistically approximate completeness (PAC) of CRSat relies on the same kind of variable ordering as the one imposed by the depth-based heuristic, and hence provides a theoretical motivation for the heuristic. Furthermore, we compare the behavior of (depth-based) CRSat to that of the justification-based SLS method BC SLS driven by justification frontiers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.917
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.329
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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