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Record W3121836531 · doi:10.46298/lmcs-18(3:14)2022

Tameness and the power of programs over monoids in DA

2022· article· en· W3121836531 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

VenueLogical Methods in Computer Science · 2022
Typearticle
Languageen
FieldComputer Science
Topicsemigroups and automata theory
Canadian institutionsUniversité de MontréalComputer Research Institute of Montréal
Fundersnot available
KeywordsMorphismMonoidClass (philosophy)Regular languageMathematicsCharacterization (materials science)HierarchyExpressive powerComplexity classPure mathematicsDiscrete mathematicsAlgebra over a fieldComputer scienceTime complexityAutomatonProgramming languagePhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

The program-over-monoid model of computation originates with Barrington's proof that the model captures the complexity class $\mathsf{NC^1}$. Here we make progress in understanding the subtleties of the model. First, we identify a new tameness condition on a class of monoids that entails a natural characterization of the regular languages recognizable by programs over monoids from the class. Second, we prove that the class known as $\mathbf{DA}$ satisfies tameness and hence that the regular languages recognized by programs over monoids in $\mathbf{DA}$ are precisely those recognizable in the classical sense by morphisms from $\mathbf{QDA}$. Third, we show by contrast that the well studied class of monoids called $\mathbf{J}$ is not tame. Finally, we exhibit a program-length-based hierarchy within the class of languages recognized by programs over monoids from $\mathbf{DA}$.

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)0.009
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 score0.753
Threshold uncertainty score0.444

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.003
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 head0.045
GPT teacher head0.357
Teacher spread0.312 · 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