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.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 4
citation-polarity summary
years
2026 3roles
background 3polarities
background 3representative citing papers
QLL is a novel logic for neuro-symbolic learning that uses ML-native operations (sum, log-sum-exp) on logits to embed constraints, satisfying most linear logic properties and showing stronger correlation between empirical robustness and formal verification than prior approaches.
VNN-LIB 2.0 defines a network theory abstraction, formal query syntax, type system over numeric domains, and Agda-mechanized semantics to provide rigorous foundations for neural network verification independent of evolving model formats.
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.
-
Quantitative Linear Logic for Neuro-Symbolic Learning and Verification
QLL is a novel logic for neuro-symbolic learning that uses ML-native operations (sum, log-sum-exp) on logits to embed constraints, satisfying most linear logic properties and showing stronger correlation between empirical robustness and formal verification than prior approaches.
-
VNN-LIB 2.0: Rigorous Foundations for Neural Network Verification
VNN-LIB 2.0 defines a network theory abstraction, formal query syntax, type system over numeric domains, and Agda-mechanized semantics to provide rigorous foundations for neural network verification independent of evolving model formats.