Performance of Ethereum 2.0-Like Consensus Under Single-Slot Finality
Bibliographic record
Abstract
Implementing a consensus protocol in a Proof-of-Stake context requires a delicate tradeoff between different system parameters. Ethereum 2.0, probably the most popular PoS system today, uses a large number of validators to achieve decentralization, but long time windows, during which both blocks and attestations for those blocks are considered valid, open up the possibility for a number of attacks that target the process of consensus. A possible remedy would be to try to achieve single-slot finality similar to that obtained in Practical Byzantine Fault Tolerance (PBFT). In this paper, we develop a Markov chain model of validator lifecycle in an Ethereum 2.0-like system with single-slot finality which includes penalties and rewards, as well as the possibility of voluntary exit and waiting to rejoin the validator pool. Using the model, we obtain the probability of achieving consensus as the function of probabilities of different events, most notably the probability of truthful voting by the validator. Our results indicate that consensus is rather sensitive to false voting, and that low probability of waiting and low probability of voluntary exit help improve the probability of consensus.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".