Efficient variant transaction injection protocols and adaptive policy optimisation for decentralised ledger systems
Why this work is in the frame
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Bibliographic record
Abstract
For decentralised cryptocurrency systems, it is important to provide users an efficient network. One performance bottleneck is the latency issue. To address this issue, we provide four protocols to utilise the resources based on the traffic in the network to alleviate the latency in the network. To facilitate the verification process, we discuss three variant injection protocols: Periodic Injection of Transaction via Evaluation Corridor (PITEC), Probabilistic Injection of Transactions (PIT) and Adaptive Semi-synchronous Transaction Inject (ASTI). The injection protocols are variants based on the given assumptions of the network. The goal is to provide dynamic injection of unverified transactions to enhance the performance of the network. The Adaptive Policy Optimisation (APO) protocols aim at optimising a cryptocurrency system's own house policy. The house policy optimisation is translated into a 0/1 knapsack problem. The APO protocol is a fully polynomial time approximation scheme for the decentralised ledger system.
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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 | 0.000 | 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 it