SpatialEvo: Self-Evolving Spatial Intelligence via Deterministic Geometric Environments
Dinging Li, Yingxiu Zhao, Xinrui Cheng, Kangheng Lin, Hongbo Peng, Hongxing Li, Zixuan Wang, Yuhong Dai, Haodong Li, Jia Wang, Yukang Shi, Liang Zhao, Jianjian Sun, Zheng Ge, Xiangyu Zhang, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen

TL;DR
SpatialEvo introduces a self-evolving framework for 3D spatial reasoning that leverages deterministic geometric properties to improve model accuracy without relying on noisy pseudo-labels.
Contribution
It formalizes a deterministic geometric environment (DGE) that enables objective, noise-free training for spatial reasoning models, surpassing prior consensus-based pseudo-label methods.
Findings
Achieves highest average scores on nine spatial reasoning benchmarks.
Demonstrates consistent improvements at 3B and 7B model scales.
Maintains performance on general visual understanding tasks.
Abstract
Spatial reasoning over three-dimensional scenes is a core capability for embodied intelligence, yet continuous model improvement remains bottlenecked by the cost of geometric annotation. The self-evolving paradigm offers a promising path, but its reliance on model consensus to construct pseudo-labels causes training to reinforce rather than correct the model's own geometric errors. We identify a property unique to 3D spatial reasoning that circumvents this limitation: ground truth is a deterministic consequence of the underlying geometry, computable exactly from point clouds and camera poses without any model involvement. Building on this insight, we present SpatialEvo, a self-evolving framework for 3D spatial reasoning, centered on the Deterministic Geometric Environment (DGE). The DGE formalizes 16 spatial reasoning task categories under explicit geometric validation rules and…
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