MétaCan
Menu
Back to cohort
Record W2963800009 · doi:10.1145/3293611.3331574

The Recoverable Consensus Hierarchy

2019· article· en· W2963800009 on OpenAlexaff
Wojciech Golab

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHierarchyComputer scienceConsensusSet (abstract data type)Asynchronous communicationTheoretical computer scienceComputabilityProcess (computing)CrashBounded functionDistributed computingMulti-agent systemMathematicsArtificial intelligenceProgramming languageComputer network

Abstract

fetched live from OpenAlex

Herlihy's consensus hierarchy ranks the power of various synchronization primitives for solving consensus in a model where asynchronous processes communicate through shared memory, and may fail by halting. This paper revisits the consensus hierarchy in a model with crash-recovery failures, where the specification of consensus, called recoverable consensus in this paper, is weakened by allowing non-terminating executions when a process fails infinitely often. Two variations of this model are considered: with independent process failures, and with simultaneous (i.e., system-wide) process failures. We prove two fundamental results: (i) Test-And-Set is at level 2 of the recoverable consensus hierarchy if failures are simultaneous, and similarly for any primitive at level 2 of the traditional consensus hierarchy; and (ii) Test-And-Set drops to level 1 of the hierarchy if failures are independent, unless the number of such failures is bounded. To our knowledge, this is the first separation between the simultaneous and independent crash-recovery failure models with respect to the computability of consensus.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.881
Threshold uncertainty score1.000

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.005
GPT teacher head0.202
Teacher spread0.197 · 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
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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicDistributed systems and fault toleranceFrench-language works237,207