通过指数倾斜的扩散引导搜索（DiffTilt）：在安全关键系统证伪中的应用
Diffusion-Guided Search via Exponential Tilting (DiffTilt): An Application to Falsification of Safety-Critical Systems
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中文总结 AI 辅助
针对自主和网络物理系统安全关键故障发现难题，提出DiffTilt框架，通过指数倾斜扩散模型诱导分布进行引导搜索，放大故障概率，优于条件采样，在基准测试中表现良好，提升了证伪性能。
中文摘要 AI 辅助
在自主和网络物理系统中发现罕见的安全关键故障是验证和确认中的一项基本挑战。现有证伪方法依赖条件采样策略，受乘法稀有效应影响。本文开发了DiffTilt，一个对扩散模型诱导的环境与执行联合分布进行指数倾斜的分布框架。扩散引导采样可解释为联合空间中的重要性采样，倾斜能放大故障概率且优于条件采样。联合生成模型可作为场景先验，系统模拟限于学习评分函数。在ARCH - COMP基准测试及新的牵引车 - 挂车基准测试中研究DiffTilt，该方法与现有方法相比证伪性能有竞争力或得到改进。
英文摘要
Discovering rare safety-critical failures in autonomous and cyber-physical systems is a fundamental challenge in verification and validation. Existing falsification approaches rely on conditional sampling strategies that factor the joint distribution over environments and system executions, and therefore suffer from multiplicative rarity effects: the simultaneous scarcity of failure-inducing inputs and failure-inducing traces makes exhaustive search prohibitively expensive. This paper develops DiffTilt, a distributional framework that exponentially tilts a diffusion model-induced joint distribution over environments and executions. We show that diffusion-guided sampling admits an exact interpretation as importance sampling in the joint space, where guidance scores induce a KL-optimal reallocation of probability mass towards failure-relevant behaviors. We further show that tilting provably amplifies failure probability and strictly outperforms conditional sampling, which is limited by multiplicative rarity. In this framework, the joint generative model serves as a reusable prior over scenarios and need not faithfully represent the system under test. Expensive system simulations are instead limited to learning a scoring function that characterizes scenario quality, enabling their selective and adaptive use. We study DiffTilt on ARCH-COMP benchmarks, and we propose an additional tractor-trailer benchmark showing the behavior of several approaches when scenario generation is guided by a well-defined specification rather than a reward. The proposed method achieves competitive or improved falsification performance compared to state-of-the-art approaches, with larger gains when specification definition is not limited to STL formulas.
发表机构
- School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）
- Toyota Motor North America, Research & Development（丰田汽车北美研发公司）
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