Probabilistic Analysis of Validator Lifecycle and Fork Resolution in Ethereum 2.0-Like PoS System
Bibliographic record
Abstract
Ethereum 2.0 uses a Proof-of-Stake-based consensus which aims to minimize the impact of malicious validators by decentralizing the voting protocol. In this paper we investigate the lifecycle of a validator in a consensus protocol similar to Ethereum 2.0 but with simplifications introduced for tractability. In particular, the protocol operates with near-single slot finality and includes the impact of behaviors such as truthful and false voting, abstention from voting, voluntary exit from the validator committee, and return to the committee upon depositing the required stake. Using probabilistic techniques and a Markov chain model, we examine the impact of all those factors on consensus probability. Our results indicate that the probability of truthful voting has a predominant effect on consensus, although the interplay between probabilities of voluntary exit and waiting before returning to the committee also plays an important role. We also investigate the process of fork resolution and model the behavior of the blockchain in the presence of multiple tips, and we show that probability of truthful voting is equally important in this case as higher values accelerate fork resolution.
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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 | 0.001 |
| 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".