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
Blockchain technology has emerged as the cornerstone of many decentralized applications operating among otherwise untrusted peers. However, it is well known that existing blockchain systems do not scale well. Transactions are often executed and committed sequentially in order to maintain the same view of the total order. Furthermore, it is necessary to duplicate both transaction data and their executions in every node in the blockchain network for integrity assurance. Such storage and computation requirements put significant burdens on the blockchain system, not only limiting system scalability but also undermining system security and robustness by making the network more centralized. To tackle these problems, in this paper, we propose SlimChain, a novel blockchain system that scales transactions through off-chain storage and parallel processing. Advocating a stateless design, SlimChain maintains only the short commitments of ledger states on-chain while dedicating transaction executions and data storage to off-chain nodes. To realize SlimChain, we propose new schemes for off-chain smart contract execution, on-chain transaction validation, and state commitment. We also propose optimizations to reduce network transmissions and a new sharding technique to improve system scalability further. Extensive experiments are conducted to validate the performance of the proposed SlimChain system. Compared with the existing systems, SlimChain reduces the on-chain storage requirements by 97% ~ 99%, while also improving the peak throughput by 1.4× ~ 15.6×.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it