Rethinking World Models for Safety-Critical Embodied Systems

2026-09-03

AI Summary

本文针对安全关键型实体系统中世界模型存在的结构不匹配问题，提出了一种基于风险信息的世界模型（RIWM），通过整合决策相关表示、反事实推理等能力来解决。

Abstract

World models have progressed from compact latent dynamics to generative, controllable, and interactive simulators of embodied environments. However, high predictive likelihood and visual fidelity do not necessarily ensure that a model preserves the evidence required for safe decision-making. This perspective identifies three structural mismatches in current world modeling: likelihood versus risk, prediction versus intervention, and finite-horizon prediction versus accumulated consequences. We propose the Risk-Informed World Model (RIWM) as a decision-centric research direction for safety-critical embodied systems. RIWM organizes world modeling around consequences, intervention, epistemic uncertainty, and recoverability, and integrates four interdependent capabilities: decision-relevant representation, counterfactual reasoning, safety-critical episodic memory, and runtime safety assurance. It distinguishes physical, social, and operational consequences while using epistemic uncertainty to qualify the evidence supporting action. We further discuss open challenges in identifying consequential futures, validating counterfactual reasoning, maintaining revisable safety memories, translating learned consequences into executable constraints, and determining when evidence is sufficient to act. This perspective argues that future world models should move beyond predicting likely futures toward identifying which futures matter, revising judgments through experience, and recognizing when to act, revise, sense, defer, or abstain.

Problem

• World Models
• Safety-Critical Systems
• Predictive Likelihood
• Risk
• Decision-Making

Innovation

• Risk-Informed World Model
• Safety-Critical Embodied Systems
• Epistemic Uncertainty
• Counterfactual Reasoning