提出快速低成本的可验证计算协议，提升AI决策可信度。
Statistical Proof of Execution (SPEX)
- 基于采样设计新协议，效率远超现有方法。
- 有效应对非确定性问题，支持常见场景稳定运行。
- 适合对AI决策可信性要求高的自动驾驶等应用。
越来越多真实应用场景正引入自动化决策，这得益于机器学习/人工智能推理在规划与引导中的广泛应用。本研究探讨了自主决策中可验证计算日益增长的需求。我们形式化了可验证计算问题，并提出一种基于采样的协议，相比现有方法显著更快、更经济且更简单。此外，针对非确定性带来的挑战，我们提出一系列策略，能有效处理常见情形。
原文摘要 · Abstract (English)
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.
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