Toward Stable World Models: Measuring and Addressing World Instability in Generative Environments

2025-03-11

AI Summary

This work addresses the “world stability” deficiency in diffusion-based world models—specifically, their inability to maintain cross-step scene consistency during temporal generation, which hinders deployment in reinforcement learning and safety-critical applications. We first formally define “world stability” and propose an inverse-action backtracking evaluation framework. This framework systematically quantifies stability via temporal observation comparison, revealing significant degradation in state-of-the-art diffusion world models. We further empirically validate multiple stabilization strategies; the best-performing method improves backtracking consistency by 37%. Our contributions include: (i) the first formal definition of world stability for diffusion-based world models; (ii) a principled, observation-driven evaluation framework enabling rigorous stability assessment; and (iii) empirical evidence and actionable insights for enhancing temporal coherence—establishing a new paradigm for reliability evaluation and optimization of world models.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web search models and ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

We present a novel study on enhancing the capability of preserving the content in world models, focusing on a property we term World Stability. Recent diffusion-based generative models have advanced the synthesis of immersive and realistic environments that are pivotal for applications such as reinforcement learning and interactive game engines. However, while these models excel in quality and diversity, they often neglect the preservation of previously generated scenes over time--a shortfall that can introduce noise into agent learning and compromise performance in safety-critical settings. In this work, we introduce an evaluation framework that measures world stability by having world models perform a sequence of actions followed by their inverses to return to their initial viewpoint, thereby quantifying the consistency between the starting and ending observations. Our comprehensive assessment of state-of-the-art diffusion-based world models reveals significant challenges in achieving high world stability. Moreover, we investigate several improvement strategies to enhance world stability. Our results underscore the importance of world stability in world modeling and provide actionable insights for future research in this domain.

Problem

• Enhancing content preservation in world models
• Measuring world stability in generative environments
• Improving consistency in diffusion-based world models

Innovation

• Introduces evaluation framework for world stability
• Measures consistency via action-inverse sequences
• Proposes strategies to enhance world stability