Vehicle enables compositional verification of neural controllers in discrete and continuous cyber-physical systems across Rocq, Isabelle/HOL, Agda, and Imandra, including the first infinite time-horizon safety proof for a continuous medical device in a general-purpose ITP.
Neural network verification with branch-and-bound for general nonlinearities
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
A ReLU-catalyzed abstraction method yields tighter bounds for transformer verification by converting dot-product constraints into ReLU forms that leverage standard convex relaxations.
A scalable verification framework for neural control barrier functions uses linear bound propagation on network gradients combined with McCormick relaxations to certify safety conditions for control-affine systems.
citing papers explorer
-
Compositional Neural-Cyber-Physical System Verification in the Interactive Theorem Prover of Your Choice
Vehicle enables compositional verification of neural controllers in discrete and continuous cyber-physical systems across Rocq, Isabelle/HOL, Agda, and Imandra, including the first infinite time-horizon safety proof for a continuous medical device in a general-purpose ITP.
-
Precise Verification of Transformers through ReLU-Catalyzed Abstraction Refinement
A ReLU-catalyzed abstraction method yields tighter bounds for transformer verification by converting dot-product constraints into ReLU forms that leverage standard convex relaxations.
-
Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation
A scalable verification framework for neural control barrier functions uses linear bound propagation on network gradients combined with McCormick relaxations to certify safety conditions for control-affine systems.