Understanding Rollout Error in Graph World Models
This paper studies long-horizon rollout error in Graph World Models (GWMs). The authors formulate a unified fixed-edge and dynamic-edge GWM framework and develop graph-valued rollout bounds to separate topology-induced from model-induced amplification. They propose Error-Aware GWM combining spectral regularization, rollout consistency, and critical-node weighting. Experiments show rollout error and planning regret grow with horizon, dynamic-edge training is needed when structure evolves, and Error-Aware GWM prevents long-horizon divergence while preserving prediction accuracy.
[2606.27780] Understanding Rollout Error in Graph World Models
[Submitted on 26 Jun 2026]
Title:Understanding Rollout Error in Graph World Models
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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-, and graph-level decisions. We develop graph-valued rollout bounds that separate topology-induced amplification from model-induced amplification, and we introduce a joint node-edge operator for dynamic-edge rollouts. Guided by the analysis, we propose Error-Aware GWM, which combines spectral regularization, rollout consistency, and critical-node weighting. Across synthetic topologies and heterogeneous agent-graph testbeds, rollout error and planning regret grow with horizon, dynamic-edge training is needed when structure evolves, and Error-Aware GWM prevents long-horizon divergence while preserving prediction accuracy. Real-world graph benchmarks clarify the scope of GWMs: they are most useful for dynamic graph rollout and agent planning, while specialized graph models remain strong on static or sparse prediction tasks.
Comments: Under Review
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.27780 [cs.AI]
(or arXiv:2606.27780v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.27780
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zekun Cai [view email] [v1] Fri, 26 Jun 2026 07:11:29 UTC (5,897 KB)
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