Rethinking World Models for Safety-Critical Embodied Systems

arXiv:2609.03774 · cs.AI, cs.RO · Submitted 2026-09-03 · Read on arXiv

cs.AI, cs.RO

Submitted: 2026-09-03

Updated: 2026-10-01

Comments: 6 pages, 2 figures. Perspective article

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• World Model — 16× in this paper · explained in 7 episodes
• Epistemic Uncertainty — 12× in this paper · explained in 10 episodes
• Risk-Informed World Model — 4× in this paper
• Fidelity — 3× in this paper · explained in 2 episodes
• Provenance — 3× in this paper · explained in 4 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Generative AI — 2× in this paper · explained in 2 episodes
• Korea Advanced Institute of Science and Technology — 2× in this paper
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Abstention — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Aleatoric Uncertainty — 1× in this paper · explained in 5 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Deferral — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Information Gain — 1× in this paper · explained in 2 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Proceedings of the European Control Conference — 1× in this paper
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• A knowledge-augmented dataset of high-risk driving scenarios with LLM annotations for autonomous driving
• GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection