Manipulation-Resistant Oracles via Continuous Peer Prediction
Abstract
Blockchain oracle networks, including decentralised physical infrastructure networks (DePINs), report physical measurements from a source that can manipulate the underlying process. A carbon project operator, for example, can dry a site before measurement and be paid for soil organic carbon (SOC) that was never stored. Peer prediction covers discrete signals under a strategic source, or continuous reports under an honest source, but not both at once. We close that gap for Gaussian reports. Strict propriety of the continuous ranked probability score (CRPS) settles the reporting stage alone. Our result concerns the source stage. A report-only settlement rule whose honest-source payoff is non-decreasing in the true value identifies and deters source manipulation if and only if the sensors respond to that manipulation by different amounts. When they respond identically, manipulation is observationally equivalent to a genuine rise in SOC, and CRPS translation equivariance leaves every peer score unchanged, so no such rule deters the source. Necessity follows from invariance rather than from the signal enumeration available in the discrete case. The source-stage estimator is ordinary weighted least squares, and the contribution is the characterisation around it. With eight calibrated SOC models on 37,280 real European soil spectra, shift-naive aggregators over-credit by up to 57% under a calibrated moisture perturbation while the joint estimate stays near-unbiased. The guarantee holds on the equilibrium path for non-colluding reporters and inherits the models' own calibration error. The project page is at https://rqcker.github.io/crps-wls.
Related
- Stochastic and Noisy Oracles in Smooth Continuous Optimization
- Manipulation-Robust Prediction
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BibTeX
@misc{song2026manipulationresistant,
title = {{Manipulation-Resistant Oracles via Continuous Peer Prediction}},
author = {Junhao Song and Tanis Rolandi and Adam Novocky and Enrico Biffis and William Knottenbelt},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.35888},
eprint = {2610.35888},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.35888},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Manipulation-Resistant Oracles via Continuous Peer Prediction
AU - Junhao Song
AU - Tanis Rolandi
AU - Adam Novocky
AU - Enrico Biffis
AU - William Knottenbelt
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.35888
UR - https://arxiv.org/abs/2610.35888
AB - Blockchain oracle networks, including decentralised physical infrastructure networks (DePINs), report physical measurements from a source that can manipulate the underlying process. A carbon project operator, for example, can dry a site before measurement and be paid for soil organic carbon (SOC) that was never stored. Peer prediction covers discrete signals under a strategic source, or continuous reports under an honest source, but not both at once. We close that gap for Gaussian reports. Strict propriety of the continuous ranked probability score (CRPS) settles the reporting stage alone. Our result concerns the source stage. A report-only settlement rule whose honest-source payoff is non-decreasing in the true value identifies and deters source manipulation if and only if the sensors respond to that manipulation by different amounts. When they respond identically, manipulation is observationally equivalent to a genuine rise in SOC, and CRPS translation equivariance leaves every peer score unchanged, so no such rule deters the source. Necessity follows from invariance rather than from the signal enumeration available in the discrete case. The source-stage estimator is ordinary weighted least squares, and the contribution is the characterisation around it. With eight calibrated SOC models on 37,280 real European soil spectra, shift-naive aggregators over-credit by up to 57% under a calibrated moisture perturbation while the joint estimate stays near-unbiased. The guarantee holds on the equilibrium path for non-colluding reporters and inherits the models' own calibration error. The project page is at https://rqcker.github.io/crps-wls.
ER -