SIMBA: An Efficient Simulator for Blockchain Applications
Why this work is in the frame
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Bibliographic record
Abstract
Predicting the performance of a blockchain application during the design phase is difficult and evaluation after it is built could be expensive. The ability to simulate a blockchain network during the design stage in order to evaluate it is therefore a necessity. In this paper, we present a simulator for blockchain applications, called SIMBA (SIMulator for Blockchain Applications). SIMBA extends an existing simulator by adding the Merkle tree feature to blockchain nodes to improve efficiency and allowing more realistic evaluations not possible with the base tool to be performed. Results of our experiments show that the inclusion of Merkle trees has a high impact of up to 30 times reduction in the verification time of block transactions without an impact on block propagation delay. Since block verification is a critical part of the computational load of nodes on the network, this performance improvement significantly affects the overall performance of each node and consequently the entire network.
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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 | 0.000 | 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 | 0.001 | 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