用可证明稳健实例压力测试神经网络验证器

基于验证器的智能体强化学习实现可证明正确的代码

Verifier-Grounded Agentic RL for Provably Correct Code

该研究提出基于验证器的智能体强化学习方法，通过读取验证器诊断、引入完整性审计及协作证明专家，使Qwen系列模型在代码验证与补丁生成任务上的准确率显著提升。

Training verified learners with learned verifiers

This paper proposes a new algorithmic framework, predictor-verifier training, to train neural networks that are verifiable, i.e., networks that provably satisfy some desired input-output properties. The key idea is to simultaneously train two networks: a predictor network that performs the task at hand,e.g., predicting labels given inputs, and a verifier network that computes a bound on how well the predictor satisfies the properties being verified. Both networks can be trained simultaneously to optimize a weighted combination of the standard data-fitting loss and a term that bounds the maximu…

Learning to Give Checkable Answers with Prover-Verifier Games

Our ability to know when to trust the decisions made by machine learning systems has not kept up with the staggering improvements in their performance, limiting their applicability in high-stakes domains. We introduce Prover-Verifier Games (PVGs), a game-theoretic framework to encourage learning agents to solve decision problems in a verifiable manner. The PVG consists of two learners with competing objectives: a trusted verifier network tries to choose the correct answer, and a more powerful but untrusted prover network attempts to persuade the verifier of a particular answer, regardless of i…

Provably Robust Adversarial Examples

We introduce the concept of provably robust adversarial examples for deep neural networks - connected input regions constructed from standard adversarial examples which are guaranteed to be robust to a set of real-world perturbations (such as changes in pixel intensity and geometric transformations). We present a novel method called PARADE for generating these regions in a scalable manner which works by iteratively refining the region initially obtained via sampling until a refined region is certified to be adversarial with existing state-of-the-art verifiers. At each step, a novel optimizatio…

可证明有界神经网络预映像

Provably Bounding Neural Network Preimages

提出INVPROP算法，用于神经网络预映像的过度近似，结合分支定界，GPU加速且无需线性规划求解器，在安全控制、对抗鲁棒性和分布外检测中应用，紧致度提升2500倍以上，速度提升2.5倍。

CertiFair: A Framework for Certified Global Fairness of Neural Networks

We consider the problem of whether a Neural Network (NN) model satisfies global individual fairness. Individual Fairness suggests that similar individuals with respect to a certain task are to be treated similarly by the decision model. In this work, we have two main objectives. The first is to construct a verifier which checks whether the fairness property holds for a given NN in a classification task or provide a counterexample if it is violated, i.e., the model is fair if all similar individuals are classified the same, and unfair if a pair of similar individuals are classified differently.…