stable-worldmodel-v1: Reproducible World Modeling Research and Evaluation

2026-02-09

AI Summary

This work addresses the limited reproducibility, absence of standardized evaluation, and lack of systematic testing in current world model research, which often relies on ad hoc, paper-specific implementations. To overcome these challenges, we propose the first unified, modular, and well-documented framework for world model research. The framework integrates standardized simulation environments, mechanisms for controlled variable manipulation, efficient data collection tools, planning algorithms, and baseline models such as DINO-WM. It enables fine-grained control over visual and physical properties, substantially enhancing experimental reproducibility and evaluation consistency. We demonstrate its effectiveness through a zero-shot robustness study of DINO-WM, confirming the framework’s utility in advancing systematic and scalable world model research.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

World Models have emerged as a powerful paradigm for learning compact, predictive representations of environment dynamics, enabling agents to reason, plan, and generalize beyond direct experience. Despite recent interest in World Models, most available implementations remain publication-specific, severely limiting their reusability, increasing the risk of bugs, and reducing evaluation standardization. To mitigate these issues, we introduce stable-worldmodel (SWM), a modular, tested, and documented world-model research ecosystem that provides efficient data-collection tools, standardized environments, planning algorithms, and baseline implementations. In addition, each environment in SWM enables controllable factors of variation, including visual and physical properties, to support robustness and continual learning research. Finally, we demonstrate the utility of SWM by using it to study zero-shot robustness in DINO-WM.

Problem

• World Models
• reproducibility
• standardization
• reusability
• evaluation

Innovation

• World Models
• Reproducibility
• Modular Framework
• Controllable Factors of Variation
• Zero-shot Robustness