Antipaxos: Taking Interactive Consistency to the Next Level
Bibliographic record
Abstract
Classical consensus protocols, such as Paxos, limit system scalability due to a single leader. This paper introduces a leaderless protocol, called Antipaxos, that reaches agreement on multiple proposals with a fast path solution in the failure-free case, and falls back on a slow path to handle other cases. We first formalize a new agreement problem, termed as k-Interactive Consistency. Then, we present two algorithms to solve this problem under the crash failure model and Byzantine failure model, respectively. We prove the safety and liveness of the proposed algorithms, and present an experimental evaluation of their performance in the Amazon cloud. Antipaxos solves the k-Interactive Consistency problem for two distinct k values under different conditions. The experiments show that our algorithms achieve not only lower execution latency but also higher peak throughput in the failure-free case when deployed in a geo-distributed environment, as compared to one of the state-of-the-art protocols, Egalitarian Paxos (EPaxos). Furthermore, both the crash-tolerant and Byzantine-tolerant designs reach agreement on n batches of proposals with Θ(n2) messages. This leads to the linear complexity of each batch in one consensus cycle, rather than a single batch of proposals per cycle in conventional solutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".