ICML 2025

Graph World Model
Tao Feng 1 Yexin Wu 1 Guanyu Lin 1 Jiaxuan You 1
Abstract
World models (WMs) demonstrate strong capa-
bilities in prediction, generation, and planning
tasks. Existing WMs primarily focus on unstruc-
tured data while cannot leverage the ubiquitous
structured data, often represented as graphs, in
the digital world. While multiple graph foun-
dation models have been proposed, they focus
on graph learning tasks and cannot extend to
diverse multi-modal data and interdisciplinary
tasks. To address these challenges, we propose the
Graph World Model (GWM), a world model that
supports both unstructured and graph-structured
states with multi-modal information and repre-
sents diverse tasks as actions.
The core of a
GWM is a generic message-passing algorithm
to aggregate structured information, either over
a unified multi-modal token space by converting
multi-modal data into text (GWM-T) or a uni-
fied multi-modal embedding space by modality-
specific encoders (GWM-E). Notably, GWM in-
troduces action nodes to support diverse tasks,
where action nodes are linked to other nodes via
direct reference or similarity computation. Exten-
sive experiments on 6 tasks from diverse domains,
including multi-modal generation and matching,
recommendation, graph prediction, multi-agent,
retrieval-augmented generation, and planning and
optimization, show that the same GWM out-
performs or matches domain-specific baselines’
performance, benefits from multi-hop structures,
and demonstrate strong zero-shot/few-shot ca-
pabilities on unseen new tasks. Our codes for
GWM is released at https://github.com/
ulab-uiuc/GWM.
1Department of Computer Science, University of Illinois Ur-
bana Champaign Urbana, IL, USA. Correspondence to: Tao Feng
<taofeng2@illinois.edu>, Jiaxuan You <jiaxuan@illinois.edu>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025
by the author(s).
1. Introduction
A world model (WM) (Ha & Schmid...