Subchains: A Technique to Scale Bitcoin and Improve the User Experience
Bibliographic record
Abstract
Orphan risk for large blocks limits Bitcoin’s transactional capacity while the lack of secure instant transactions restricts its usability. Progress on either front would help spur adoption. This paper considers a technique for using fractional-difficulty blocks (weak blocks) to build subchains bridging adjacent pairs of real blocks. Subchains reduce orphan risk by propagating blocks layer-by-layer over the entire block interval, rather than all at once when the proof-of-work is solved. Each new layer of transactions helps to secure the transactions included in lower layers, even though none of the transactions have been con-firmed in a real block. Miners are incentivized to cooperate building subchains in order to process more transactions per second (thereby claiming more fee revenue) without incur-ring additional orphan risk. The use of subchains also diverts fee revenue towards network hash power rather than dripping it out of the system to pay for orphaned blocks. By nesting subchains, weak block verification times approaching the theoretical limits imposed by speed-of-light constraints would become possible with future technology improvements. As subchains are built on top of the existing Bitcoin protocol, their implementation does not require any changes to Bitcoin’s consensus rules.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".