Blockchain Scaling Using Rollups: A Comprehensive Survey
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 systems have seen much growth in recent years due to the immense potential attributed to the technology behind these systems. However, this popularity has outlined a critical scalability issue that most blockchain systems are now confronted with. With their increasing popularity comes an increasing amount of load on the system. Several scaling solutions that modify either the functioning of the underlying protocol or that build on top of them have already been proposed; however, each of these solutions comes with their advantages and disadvantages. This paper aims to survey the current state-of-the-art of rollups as a scaling solution. We discuss the mode of operation of the different types of rollups, outline state-of-the-art implementations of each type together with their features and limitations. We also conduct a performance analysis comparing these implementations. Finally, we outline avenues for future research around rollups as a scaling solution.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 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