Candidate Set Formation Policy for Mining Pools
Bibliographic record
Abstract
Cryptocurrencies like Bitcoin use the blockchain technology to record transactions in a distributed and secure way. Miners are distributed entities responsible for validating the transactions in the network and producing and verifying new blocks of confirmed transactions. In addition, they are responsible for keeping the protocol’s integrity, protecting it against the double-spending problem. Miners are rewarded when they add new blocks to the blockchain. The reward consists of fresh coins created after adding a new block as well as the fees collected from the transactions inside the added block. The amount of new coins generated with new blocks is diminishing by the protocol over time. As such, the significance of collected fees is increasing for the miners.A recent trend in large mining pools is to allow miners to select transactions in the block they aim to mine. Allowing miners to select transactions increases transparency via discretization and also helps to avoid conflict of interest with the mining pool managers. Assuming that forming blocks is in miners’ hands, miners should have a strategy to maintain transactions inside the block in a way to maximize their collected fees. The mining process is a random process that is "progress free". That is, a miner can update the transactions inside the block without any impact on its chance of succeeding in adding the block. Given that transactions with higher fees might arrive at any time in an online manner, it is profitable for a miner to "refresh" its block during the mining process. In this paper, we study the impact of refreshing blocks via an experimental analysis on real-world data from Bitcoin on a controlled environment that is carefully tuned to match the real world. Our results indicate that refreshing blocks is essential for increasing miners’ collected fees, and overlooking it will have a significant impact on miners’ revenues.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".