An Improved PBFT Consensus Algorithm Based on the Raft Voting Mechanism and DAG Ledger Structure
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
To address the issues of high communication overhead, low throughput, and arbitrary primary node selection in the traditional PBFT consensus algorithm, this paper proposes an improved PBFT consensus algorithm based on the Raft voting mechanism and the DAG ledger structure. By introducing a two-layer architecture composed of proxy nodes and candidate nodes, the system nodes are reorganized. Within each proxy domain, a proxy primary node is elected using the Raft voting mechanism, thereby enhancing the stability and efficiency of primary node transitions. During the consensus process, leveraging the DAG ledger structure enables parallel block generation, which is divided into two phases: data block consensus and address block consensus. Digital signatures and hash commitment mechanisms are introduced in each phase to ensure message integrity and the verifiability of consensus data. Simulation results demonstrate that the improved algorithm achieves lower transaction latency and higher throughput compared to the original PBFT algorithm.
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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 | 0.001 |
| 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 | 0.000 |
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