MétaCan
Menu
Back to cohort
Record W2561581089 · doi:10.1145/3170492.3136047

Refining semantics for multi-stage programming

2017· article· en· W2561581089 on OpenAlexaff
Rui Ge, Ronald Garcia

Bibliographic record

VenueACM SIGPLAN Notices · 2017
Typearticle
Languageen
FieldComputer Science
TopicLogic, programming, and type systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceProgramming languageSemantics (computer science)Operational semanticsRefining (metallurgy)Denotational semantics

Abstract

fetched live from OpenAlex

The multi-stage programming paradigm supports runtime code generation and execution. Though powerful, its potential is impeded by the lack of static analysis support. Van Horn and Might proposed a general-purpose approach to systematically develop static analyses by transforming an environmental abstract machine, which evolves a control string, an environment and a continuation as a program evaluates. To the best of our knowledge, no such semantics exists for a multi-stage language like MetaML. We develop and prove correct an environmental abstract machine semantics for MetaML by gradually refining the reference substitutional structural operational semantics. Highlights of our approach include leveraging explicit substitutions to bridge the gap between substitutional and environmental semantics, and devising meta-environments to model the complexities of variable bindings in multi-stage environmental semantics.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.873
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0030.001
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 head0.151
GPT teacher head0.355
Teacher spread0.204 · 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.

Study designNot applicable
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

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueACM SIGPLAN NoticesSame topicLogic, programming, and type systemsFrench-language works237,207