The State-of-the-Art and Promising Future of Blockchain Sharding
Bibliographic record
Abstract
Blockchain sharding is a significant technical area, improving the scalability of blockchain systems. It is regarded as one of the potential solutions that can achieve on-chain scaling, and significantly improve the scalability of blockchains without alleviating the decentralization feature of blockchain. To provide a reference and inspire participation from both the academic and industrial sectors in the area of blockchain sharding, we have researched the state-of-the-art studies published in the past three years. We have also conducted experiments to show the performance of representative sharding protocols such as Monoxide, LBF, Metis, and BrokerChain. We envision the potential challenges and promising future of sharding techniques in terms of the urgent demands of high throughput required by emerging applications such as Web3, Metaverse, and Decentralized Finance (DeFi). We hope that this article is helpful to researchers, engineers, and educators, and will inspire subsequent studies in the field of blockchain sharding.
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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 | 0.002 | 0.001 |
| 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".