Truthful Decentralized Blockchain Oracles
Bibliographic record
Abstract
Summary Blockchain systems rely on oracles to bridge external information to the decentralized applications residing in the systems. Astraea protocols are decentralized oracle designs utilizing majority‐voting mechanism to determine the oracle outcomes and/or rewards to voters. However, the voters are indifferent between voting through a single or multiple identities, as the potential rewards by the decentralized oracles grow linearly with the voters stakes. Additionally, the majority‐voting mechanism may facilitate herd behaviors among the voters, as the voters are rewarded only if they are in agreement with the majority outcomes. In this paper, a novel oracle protocol is introduced by proposing a peer prediction‐based scoring scheme along with non‐linear staking rules, aiming at extracting subjective data truthfully. Specifically, an incentive compatible scoring scheme is designed so that voters uniquely maximize their expected score by honest reporting. The voters are rewarded when their report achieves a relatively high score compared to the rest of the voters, as opposed to the existing schemes, where a reward is only given when they agree to the majority. Furthermore, a non‐linear stake scaling rule is proposed to discourage Sybil attacks. Detailed simulation results are presented to show the operation of the proposed oracle protocol and its improvement compared to indicative mechanisms proposed in the existing literature.
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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) | 0.000 | 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".