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Record W2166178773 · doi:10.1145/330855.330947

Formal hardware verification by integrating HOL and MDG

2000· article· en· W2166178773 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicFormal Methods in Verification
Canadian institutionsNortel (Canada)Université de MontréalConcordia University
FundersConcordia University
KeywordsHOLAutomated theorem provingFormal equivalence checkingComputer scienceFormal verificationIntelligent verificationAutomated proof checkingModel checkingProgramming languageFormal methodsAbstractionVerificationFunctional verificationEquivalence (formal languages)Runtime verificationHigh-level verificationMathematicsSoftwareSoftware developmentSoftware constructionDiscrete mathematics

Abstract

fetched live from OpenAlex

In order to overcome the limitations of automated tools and the cumbersome proof process of interactive theorem proving, we adopt a hybrid approach for formal hardware verification which uses the strengths of theorem proving (HOL) with powerful mathematical tools such as induction and abstraction, and the advantages of automated tools (MDG) which support equivalence checking and model checking. The MDG system is a decision diagram based verification tool, primarily designed for hardware verification. HOL is a theorem prover built on higher-order logic.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.909
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.263
Teacher spread0.249 · 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

Quick stats

Citations12
Published2000
Admission routes2
Has abstractyes

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