自动生成神经网络规格，提升安全系统可靠性。
AutoSpec: Automated Generation of Neural Network Specifications
- 基于树结构自适应划分输入空间，生成符合模型行为的规格集。
- 通过统计认证提供严格准确率保障，F1得分比人工定义高53%。
- 提出可解释评估指标，适合安全关键领域研究者使用。
神经网络在学习增强系统中的广泛应用，凸显了模型安全与鲁棒性的需求，尤其是在安全敏感领域。尽管近期神经网络验证技术提供了最坏情况行为的形式化保证，但现有方法需用户手动定义模型规格，这一过程易出错、不完整且耗时。本文提出AutoSpec，首个针对学习增强系统自动生成与评估神经网络规格的综合框架。AutoSpec引入基于树的算法，自适应划分输入空间以生成与模型行为一致的规格集合，并设计统计认证框架，为每个规格提供严格的准确率保证。我们还提出一个有原则的评估框架，定义可解释的规格准确率与覆盖度指标，为未来研究建立基准。在四个不同应用场景的实验表明，AutoSpec优于人工定义规格和现有基线算法，相比人工规格的F1得分提升最高达53%，相比最强基线提升73%。
原文摘要 · Abstract (English)
The increasing adoption of neural networks in learning-augmented systems highlights the growing need for model safety and robustness, especially in safety-critical domains. While recent advances in neural network verification offer formal guarantees on worst-case behavior, existing approaches require users to manually define model specifications, an error-prone, incomplete, and time-consuming process. In this paper, we present AutoSpec, the first comprehensive framework for automatically generating and evaluating neural network specifications for learning-augmented systems. AutoSpec introduces a tree-based algorithm that adaptively partitions the input space to generate specification sets aligned with model behavior, as well as a statistical certification framework that provides rigorous accuracy guarantees for each specification. We also propose a principled evaluation framework that defines interpretable metrics for specification accuracy and coverage, establishing a benchmark for future research. Experiments across four diverse applications show that AutoSpec outperforms both manually defined specifications and existing baseline algorithms, improving the F1 score by up to 53% over human-defined specifications and 73% over the strongest baseline.
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