Chronos: An Efficient Asynchronous Byzantine Ordered Consensus
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.
Bibliographic record
Abstract
Abstract Byzantine ordered consensus, introduced by Zhang et al. (OSDI 2020), is a new consensus primitive that additionally guarantees a correctness specification of transaction order, allowing nodes to assign fairly an ordering indicator to the committed transaction. Zhang et al. also presented a concrete Byzantine ordered consensus protocol called Pompē in the partially synchronous network model. However, Pompē cannot prevent an adversary from manipulating message delivery time. In this paper, we present Chronos, the first Byzantine ordered consensus protocol in the asynchronous network model, where an adversary can arbitrarily manipulate message delivery time. To construct Chronos, we propose a variant of asynchronous common subset called signal asynchronous common subset protocol, which guarantees the liveness of Chronos. We implement both Chronos and its baseline HoneyBadgerBFT using Go language and deploy them on 100 Amazon t3.medium instances distributed throughout 10 regions across the world. The experimental results show that Chronos is more efficient than HoneyBadgerBFT for small network, achieving peak throughput of 59 368 tx/s when the batch size is 100 000 and the number of nodes is 4, while the peak of HoneyBadgerBFT is 57 077 tx/s.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it