RNBFT: Leveraging Randomness to Achieve Scalable Byzantine Consensus
Bibliographic record
Abstract
In this paper, we present Random Network Byzantine Fault Tolerance (RNBFT), a novel, partially synchronous, Byzantine Fault Tolerance (BFT) consensus protocol aimed towards large-scale consortium blockchain networks.The essence of RNBFT lies in the random network communication paradigm which reduces the communication overhead of the system and is further enhanced by leveraging aggregation of multi-signatures backed with an optimized gossip paradigm. This approach collectively results in achieving high throughput and efficiency which can be scaled easily with large-size quorums. With a series of experiments and analysis, we affirm that RNBFT is an ideal choice for large-size consortium networks. Thus, RNBFT promises resiliency over both major and minor arbitrary failures in nodes with a fair trade-off between the performance and scalability of the system.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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.
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".