arXiv:2606.27780v1 Announce Type: new Abstract: World models are often used for planning by rolling learned dynamics forward. Many planning environments, however, are not vectors or images; they are graphs of agents, tools, skills, routes, and dependencies. In these settings, a local prediction error may stay local or spread through the graph, and the failure mode changes again when edges are predicted rather than fixed. This paper studies long-horizon rollout error in Graph World Models (GWMs). We formulate a unified fixed-edge and dynamic-edge GWM framework with action nodes for node-, edge-
This paper addresses a critical, current challenge in the development of sophisticated AI agents and world models: understanding and mitigating rollout error in graph-structured environments.
Improving the accuracy and reliability of Graph World Models can unlock more effective long-horizon planning and decision-making for AI in complex, real-world scenarios, accelerating the capabilities of AI agents.
This research provides a foundational framework for analyzing and improving GWMs, moving beyond vector/image-centric models to better handle relational data structures inherent in many AI applications.
- · AI agents developers
- · Robotics
- · Logistics and supply chain optimization
- · Complex systems modeling
- · AI systems relying solely on vector/image models for graph-structured data
More robust and generalizable AI models capable of planning in highly interconnected environments.
Accelerated development of AI systems for tasks requiring intricate understanding of relationships, such as scientific discovery or urban planning.
Potential for new AI applications that were previously intractable due to limitations in modeling complex relational dynamics.
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Read at arXiv cs.AI