5 papers
Not All Invariants Are Equal: Curating Training Data to Accelerate Program Verification with SLMs
Ido Pinto, Yizhak Yisrael Elboher, Haoze Wu +2
The synthesis of inductive loop invariants remains a critical bottleneck in automated program verification. While Large Language Models (LLMs) show promise in mitigating this issue…
Neural Network Verification using Partial Multi-Neuron Relaxation
Ido Shmuel, Guy Katz
The increasing integration of deep neural networks in critical systems has spawned a theoretical and practical interest in formally guaranteeing safety properties about their behav…
Incremental Neural Network Verification via Learned Conflicts
Raya Elsaleh, Liam Davis, Haoze Wu +1
Neural network verification is often used as a core component within larger analysis procedures, which generate sequences of closely related verification queries over the same netw…
FAME: Formal Abstract Minimal Explanation for Neural Networks
Ryma Boumazouza, Raya Elsaleh, Melanie Ducoffe +2
We propose FAME (Formal Abstract Minimal Explanations), a new class of abductive explanations grounded in abstract interpretation. FAME is the first method to scale to large neural…
Talking with Verifiers: Automatic Specification Generation for Neural Network Verification
Yizhak Y. Elboher, Reuven Peleg, Zhouxing Shi +2
Neural network verification tools currently support only a narrow class of specifications, typically expressed as low-level constraints over raw inputs and outputs. This limitation…