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Record W4324291633 · doi:10.1109/tse.2023.3256939

New Techniques for Static Symmetry Breaking in Many-Sorted Finite Model Finding

2023· article· en· W4324291633 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.

Bibliographic record

VenueIEEE Transactions on Software Engineering · 2023
Typearticle
Languageen
FieldComputer Science
TopicMachine Learning and Algorithms
Canadian institutionsUniversity of WaterlooUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceSymmetry breakingCorrectnessTheoretical computer sciencesortSymmetry (geometry)Context (archaeology)InferenceAlgorithmMathematicsArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Symmetry in finite model finding problems of many-sorted first-order logic (MSFOL) can be exploited to reduce the number of interpretations considered during search, thereby improving solver performance for tools such as the Alloy Analyzer. We present a framework to soundly compose static symmetry breaking schemes for many-sorted finite model finding. Then, we introduce and prove the correctness of three static symmetry breaking schemes for MSFOL: 1) one for functions with distinct sorts in the domain and range; 2) one for functions where the range sort appears in the domain; and 3) one for predicates. We provide a novel presentation of sort inference in the context of symmetry breaking that yields a new mathematical link between sorts and symmetries. We empirically investigate how our symmetry breaking approaches affect solving performance.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.330
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.264
Teacher spread0.246 · 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