Elasticoin: Low-Volatility Cryptocurrency with Proofs of Sequential Work
Bibliographic record
Abstract
Blockchain-based cryptocurrencies have gained increasing adoption in recent years, and many hope that they may usher in a new era of decentralized electronic money. Unfortunately, they perform the core functions of money quite poorly due to their extremely volatile market value. On the other hand, blockchain “stablecoins” aiming to reduce this volatility, usually through a peg to an external currency like the US dollar, tend to greatly sacrifice decentralization of the money supply that make cryptocurrencies so attractive in the first place. Elasticoin is a novel currency issuance algorithm which greatly reduces price volatility by using the cryptographic puzzle of proofs of sequential work to fix the cost of minting a coin to sequential computation time. This causes coin supply to be highly elastic, quickly responding to demand for new coins and greatly dampening price swings. We argue that Elasticoin's fixed-minting-cost approach to low volatility has significant advantages over using pegs or explicit measurement of demand to adjust supply.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".