Visual world models increasingly support spatial reasoning by generating RGB views of unobserved states. However, RGB has become the default interface largely because it is well suited for visual generation, rather than necessarily being the most effective representation for downstream reasoning. Different spatial questions may benefit from different representations of the same imagined state: top-down geometry can facilitate global distance comparisons, while panoramic views provide broader context for reasoning about open space. To isolate the role of representation in spatial imagination, we introduce ImagiRep, a simulator-grounded benchmark of 922 questions spanning seven question types across two settings, Viewpoint Imagination and Cross-view Integration, providing oracle RGB and six alternative representations (e.g., depth, panoramic views, and top-down geometry) of the same unobserved state. This controlled comparison reveals that even accurate imagined RGB does not guarantee accurate spatial reasoning, and different questions benefit from different representations. Similar trends hold for representations derived from world-model-estimated states. Notably, estimated RGB alone can mislead spatial reasoning, while alternative representations retain complementary benefits. Building on these findings, we introduce GEARS, a lightweight test-time strategy for adaptive representation selection, outperforming existing spatial imagination methods and showing that reasoning about unseen states requires more than generating RGB views.
Benchmark
From a single starting view and an instructed movement (one to three moves, 0.5–9 m), reason about the new viewpoint: open-space and nearest-obstacle direction, visibility, nearest and farthest object, and where a named object lies. 200 trajectories across 73 scenes.
From two or three partially overlapping views, combine evidence about objects that never appear together: cross-view nearest and farthest object, and the left-to-right order of objects from a given view. 143 view groups across 21 scenes.
Examples
Findings
Accurate imagined RGB helps overall, but can hurt specific questions. Oracle endpoint RGB adds roughly 8–12 points over the observed views.
Alternative representations add complementary evidence, and no single one wins everywhere. Every representation recovers some errors made with oracle RGB. The best one depends on the question: top-down geometry for Farthest Object, panorama for Open-space Direction, depth for cross-view Nearest Object. Picking the right one per question (oracle selection) would reach 87.5% and 91.7% with GPT-6.1-sol.
The benefit survives imperfect imagination. With states estimated by world models (HY-World 2.0 and Stable Virtual Camera), estimated RGB alone lowers Viewpoint Imagination accuracy from 57.2% to 54.7%, while every alternative representation built from the same estimated scene lifts it to 61.7–65.6%.
Method
Gated Evidence and Adaptive Representation Selection is a lightweight test-time strategy. If the answers with and without the estimated endpoint RGB agree, GEARS keeps that answer. If they disagree, the reasoner chooses which evidence to trust, from the observed views alone to an estimated representation, and answers with it.
All methods use GPT-6.1-sol as the final reasoner and are evaluated on the same questions. MindJourney uses its released Search World Model; AVIC uses Stable Virtual Camera.
More examples
Citation
@article{zhao2026imagirep,
title = {Representing the Unseen: Rethinking Visual Imagination for Spatial Reasoning},
author = {Zhao, Zengqi and Wang, Zun and Yu, Shoubin and Bansal, Mohit and Zhang, Yue},
journal = {arXiv preprint},
year = {2026}
}