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Record W2580735446 · doi:10.3233/sat190111

Hard satisfiable 3-SAT instances via autocorrelation

2016· article· en· W2580735446 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal on Satisfiability Boolean Modeling and Computation · 2016
Typearticle
Languageen
FieldComputer Science
TopicFormal Methods in Verification
Canadian institutionsWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAutocorrelationBoolean satisfiability problemReduction (mathematics)SatisfiabilityComputer scienceMathematicsCombinatoricsAlgorithmStatistics

Abstract

fetched live from OpenAlex

We establish a reduction of a combinatorial problem defined via autocorrelation to an instance of Boolean satisfiability. As a consequence, we obtain a family of hard satisfiable 3-SAT instances. The combinatorial problem that we reduce is the D-optimal matrices problem. We generated a family of 3-SAT instances from the D-optimal matrices problem with the motivation to solve interesting cases using the power of SAT solvers. We give a detailed construction of the generated instances that were submitted to SAT competition 2014. Our reduction techniques is fairly straightforward and can be adapted to various other problems that are defined via autocorrelation.

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.

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)0.000
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: none
Teacher disagreement score0.862
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.039
GPT teacher head0.292
Teacher spread0.253 · 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