Understanding Rollout Error in Graph World Models
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).
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Computer Science > Artificial Intelligence arXiv:2606.27780 (cs) [Submitted on 26 Jun 2026] Title:Understanding Rollout Error in Graph World Models Authors:Xinyuan Song, Zekun Cai View a PDF of the paper titled Understanding Rollout Error in Graph World Models, by Xinyuan Song and 1 other authors View PDF HTML (experimental) 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).
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