Bibliographic record
Abstract
The Lightning Network (LN) is a pioneering payment channel network developed to address Bitcoin’s scalability challenges. In LN, source nodes select transaction paths without knowing intermediary channel balances as channel balances are known only to their owners. Consequently, payments often fail when channels lack sufficient funds, forcing the source to retry the entire transaction. This paper introduces Bucket, a novel atomic-payment solution designed to reduce latency by minimizing these retries without requiring nodes to share their channel balances or the source node to lock additional funds. Bucket achieves this by routing multiple independent payment alternatives simultaneously in a “bucket,” each with its own route. Intermediate nodes decrypt all alternatives, select a viable next hop based on their local channel balance knowledge, group relevant alternatives, and forward them in a new bucket. If a chosen route becomes blocked, Bucket supports efficient backtracking by enabling immediate previous nodes (not necessarily the source) to quickly select alternative paths. Simulation results using real $\mathbf{L N}$ data demonstrate that Bucket significantly reduces latency and improves transaction success rates, especially in challenging routing scenarios.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".