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Record W4411277043 · doi:10.1145/3732772.3733518

Byzantine Agreement with Predictions

2025· article· en· W4411277043 on OpenAlexafffund
Naama Ben-David, Muhammad Ayaz Dzulfikar, Faith Ellen, Seth Gilbert

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Education, IndiaMinistry of Education - Singapore
KeywordsByzantine architectureComputer scienceAncient historyHistory

Abstract

fetched live from OpenAlex

We study the problem of Byzantine Agreement with predictions in synchronous message passing systems. Along with a proposal, each process is also given a prediction, i.e., extra information that is not guaranteed to be true. For example, one might imagine that the prediction is produced by a network security monitoring service that looks for patterns of malicious behavior.

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)0.012
metaresearch head score (Gemma)0.067
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 score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.067
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.002
Science and technology studies0.0050.007
Scholarly communication0.0060.016
Open science0.0030.007
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0070.002

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 head0.220
Teacher spread0.215 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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Same topicDistributed systems and fault toleranceFrench-language works237,207