Many real-world applications are increasingly incorporating automated decision-making, driven by the widespread adoption of ML/AI inference for planning and guidance. This study examines the growing need for verifiable computing in autonomous decision-making. We formalize the problem of verifiable computing and introduce a sampling-based protocol that is significantly faster, more cost-effective, and simpler than existing methods. Furthermore, we tackle the challenges posed by non-determinism, proposing a set of strategies to effectively manage common scenarios.
@article{arxiv.2503.18899,
title = {Statistical Proof of Execution (SPEX)},
author = {Michele Dallachiesa and Antonio Pitasi and David Pinger and Josh Goodbody and Luis Vaello},
journal= {arXiv preprint arXiv:2503.18899},
year = {2025}
}