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Record W4412989191 · doi:10.1145/3747519

Fusing Session-Typed Concurrent Programming into Functional Programming

2025· article· en· W4412989191 on OpenAlexafffund
Chuta Sano, Deepak Garg, Ryan Kavanagh, Brigitte Pientka, Bernardo Toninho

Bibliographic record

VenueProceedings of the ACM on Programming Languages · 2025
Typearticle
Languageen
FieldComputer Science
TopicLogic, programming, and type systems
Canadian institutionsUniversité du Québec à MontréalMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceSession (web analytics)Programming languageFunctional reactive programmingFunctional logic programmingProgramming paradigmInductive programmingWorld Wide Web

Abstract

fetched live from OpenAlex

We introduce FuSes , a Fu nctional programming language that integrates Ses sion-typed concurrent process calculus code. A functional layer sits on top of a session-typed process layer. To generate and reason about open session-typed processes, the functional layer uses the contextual box modality extended with linear channel contexts. Due to the fundamental differences between the operational semantics of the functional layer and the concurrent semantics of processes, we bridge the two layers using a set of primitives to run and observe the behavior of closed processes within the functional layer. In addition, FuSes supports code analysis and manipulation of open session-typed process code. To showcase its benefit to programmers, we implement well-known optimizations, such as batch optimizations, as type-safe metaprograms over concurrent processes. Our technical contributions include a type system for FuSes , an operational semantics, a proof of its type safety, and an implementation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0030.006
Open science0.0020.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.285
Teacher spread0.265 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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
Published2025
Admission routes2
Has abstractyes

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Same venueProceedings of the ACM on Programming LanguagesSame topicLogic, programming, and type systemsFrench-language works237,207