MindJourney: Test-Time Scaling with World Models for Spatial Reasoning
Authors: name: Yuncong Yang, name: Jiageng Liu, name: Zheyuan Zhang, name: Siyuan Zhou, name: Reuben Tan, name: Jianwei Yang, name: Yilun Du, name: Chuang Gan

Spatial reasoning in 3D space is central to human cognition and indispensable
for embodied tasks such as navigation and manipulation. However,
state-of-the-art vision-language models (VLMs) struggle frequently with tasks
as simple as anticipating how a scene will look after an egocentric motion:
they perceive 2D images but lack an internal model of 3D dynamics. We therefore
propose MindJourney, a test-time scaling framework that grants a VLM with this
missing capability by coupling it to a controllable world model based on video
diffusion. The VLM iteratively sketches a concise camera trajectory, while the
world model synthesizes the corresponding view at each step. The VLM then
reasons over this multi-view evidence gathered during the interactive
exploration. Without any fine-tuning, our MindJourney achieves over an average
8% performance boost on the representative spatial reasoning benchmark SAT,
showing that pairing VLMs with world models for test-time scaling offers a
simple, plug-and-play route to robust 3D reasoning. Meanwhile, our method also
improves upon the test-time inference VLMs trained through reinforcement
learning, which demonstrates the potential of our method that utilizes world
models for test-time scaling.