arXiv AI
Aug 3
Learning Lookahead Lemmas for Neural Network Verification
arXiv:2607. 29051v1 Announce Type: cross Abstract: State-of-the-art neural network verifiers use the branch-and-bound procedure as their core solving mechanism.
The Luna Bound Propagator for Formal Analysis of Neural Networks
arXiv:2603. 23878v3 Announce Type: replace-cross Abstract: The parameterized CROWN analysis, a.
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arXiv AI
Jul 21
Lookahead Branching for Neural Network Verification
arXiv:2607. 17290v1 Announce Type: cross Abstract: In this work, we investigate the effect of lookahead branching strategies in neural network verification.
Hugging Face Trending Papers
Jul 19
Lookahead Branching for Neural Network Verification
In this work, we investigate the effect of lookahead branching strategies in neural network verification. We present a general recipe to integrate lookahead into any branch-and-bound verifier and demonstrate how one of the current state-of-the-art branching heuristics, FSB, can be viewed as a special instantiation of the lookahead branching strategy.
arXiv Machine Learning
Sep 23
Exploring Solver-Level Warmstarting for Neural Network Verification
arXiv:2609.25962v1 Announce Type: new Abstract: Neural network verification has become a key tool for providing formal guarantees on the behaviour of neural networks. However, many verification probl...
arXiv AI
Jun 11
\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party
arXiv:2505. 17623v2 Announce Type: replace-cross Abstract: Verifiable computing (VC) has gained prominence in decentralized machine learning systems, where resource-intensive tasks like deep neural network (DNN) inference are offloaded to external participants due to blockchain limitations.
arXiv Machine Learning
Aug 11
Floating-Point Neural Network Verification at the Software Level
arXiv:2510. 23389v2 Announce Type: replace-cross Abstract: The behaviour of neural network components must be proven correct before deployment in safety-critical systems.
arXiv Machine Learning
Aug 19
Certified but Private: Scalable Zero-Knowledge Proofs for Neural Network Guarantees
PANDA is a scalable system that uses zero‑knowledge proofs to certify the robustness and fairness of neural networks without revealing their private parameters. Built on the CROWN robustness framework, PANDA introduces a novel algorithm for proving linear relaxation bounds on non‑linear activation layers, producing lightweight proofs. The system can generate proofs for networks with over 2.9 million parameters in just five minutes and verify them in ten seconds, scaling polynomially with network size and enabling verification of models four orders of magnitude larger than prior ZKP‑based approaches.
arXiv AI
Jun 9
TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks
arXiv:2510. 16028v4 Announce Type: replace-cross Abstract: Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces).
arXiv Machine Learning
Aug 26
Encrypted Neural Networks without Overflows
arXiv:2605.23096v2 Announce Type: replace-cross Abstract: The popular Cheon-Kim-Kim-Song (CKKS) scheme enables efficient private inference in neural networks by evaluating them on encrypted data. Sin...
arXiv Machine Learning
Sep 22
PICID: Proof-Driven Clause Learning in Neural Network Verification
arXiv:2503.12083v3 Announce Type: replace-cross Abstract: Current Deep Neural Network (DNN) verifiers are typically designed to prioritize scalability over reliability. Reliability can be reinforced...
arXiv AI
Jun 18
Veriphi: Attack-Guided Neural Network Verification with Dataset-Dependent Training Methods
arXiv:2606. 18454v1 Announce Type: cross Abstract: We present Veriphi, a GPU-accelerated neural network verification system that combines fast adversarial attacks with formal bound certification using alpha,beta-CROWN methods.
arXiv AI
Sep 25
NNV3: Expanding Neural Network Verification to New Architectures and Domains
NNV3 is the latest version of the Neural Network Verification tool, a MATLAB framework for formally verifying deep learning models and learning‑enabled cyber‑physical systems. It builds on earlier NNV releases by adding new Star‑set members—ModelStar for weight perturbation, VolumeStar for video and 3D volumetric inputs, and GraphStar for graph neural networks—alongside a probabilistic reachability mode and FairNNV for fairness certification. The update also introduces benchmarks in malware detection, power‑system modeling, medical imaging, variable‑length time series, and action recognition, and provides unified documentation and tutorials.