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Record W4401597219 · doi:10.1145/3674643

Blame-Correct Support for Receiver Properties in Recursively-Structured Actor Contracts

2024· article· en· W4401597219 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

VenueProceedings of the ACM on Programming Languages · 2024
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsBlameComputer sciencePsychologyProcess managementBusinessSocial psychology

Abstract

fetched live from OpenAlex

Actor languages model concurrency as processes that communicate through asynchronous message sends. Unfortunately, as the complexity of these systems increases, it becomes more difficult to compose and integrate their components. This is because of assumptions made by components about their communication partners which may not be upheld when they remain implicit. In this paper, we bring design-by-contract programming to actor programs through a contract system that enables expressing constraints on receiver-related properties. Expressing properties about the expected receiver of a message, and about this receiver’s communication behavior, requires two novel types of contracts. Through their recursive structure, these contracts can govern entire communication chains. We implement the contract system for an actor extension of Scheme, describe it formally, and show how to assign blame in case of a contract violation. Finally, we prove our contract system and its blame assignment correct by formulating and proving a blame correctness theorem.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.015
GPT teacher head0.262
Teacher spread0.247 · 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