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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.18899 |
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| _version_ | 1866910890362667008 |
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| author | Dallachiesa, Michele Pitasi, Antonio Pinger, David Goodbody, Josh Vaello, Luis |
| author_facet | Dallachiesa, Michele Pitasi, Antonio Pinger, David Goodbody, Josh Vaello, Luis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18899 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Statistical Proof of Execution (SPEX) Dallachiesa, Michele Pitasi, Antonio Pinger, David Goodbody, Josh Vaello, Luis Artificial Intelligence Cryptography and Security 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. |
| title | Statistical Proof of Execution (SPEX) |
| topic | Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2503.18899 |