Stochastic World Models for Verifying Vision-Based Neural Feedback Systems

arXiv:2609.38120 · cs.AI, cs.SY, eess.SY · Submitted 2026-09-29 · Read on arXiv

cs.AI, cs.SY, eess.SY

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• World Model — 59× in this paper · explained in 7 episodes
• Verification — 46× in this paper · explained in 2 episodes
• Benchmark — 34× in this paper · explained in 2 episodes
• Abstraction — 22× in this paper · explained in 1 episode
• Verifier — 17× in this paper · explained in 2 episodes
• Fidelity — 11× in this paper · explained in 2 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 4× in this paper
• Robustness — 4× in this paper · explained in 5 episodes
• Steering — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Formal Methods — 2× in this paper · explained in 3 episodes
• In Computer Aided Verification — 2× in this paper
• In NASA Formal Methods — 2× in this paper
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Formal Verification — 1× in this paper · explained in 2 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• In IEEE Conference on Decision and Control — 1× in this paper
• Verifiability — 1× in this paper · explained in 2 episodes

Sources

• Polyhedral Enclosures: An Efficient Combinatorial Abstraction for Nonlinear Neural Feedback Systems
• The FABRIC Strategy for Verifying Neural Feedback Systems
• Closing the Loop: Branch-and-Bound for Scalable Verification of Nonlinear Neural Feedback Systems
• Deterministic World Models for Closed-loop Reachability Analysis of End-to-End Vision-based Control
• Counterexample Guided Branching via Directional Relaxation Analysis in Complete Neural Network Verification
• Conditional Generative Adversarial Nets
• The Era of End-to-End Autonomy: Transitioning from Rule-Based Driving to Large Driving Models
• Four Principles for Physically Interpretable World Models
• RampoNN: A Reachability-Guided System Falsification for Efficient Cyber-Kinetic Vulnerability Detection

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