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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.015 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 source (direct Gemma or distilled Codex), 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".