| title | Unfold The World: Factorize 4D Properties in Reinforcing Spatial Reasoning |
|---|---|
| authors | Yijun Yang, Shenghe Zheng, Wenbo Li, Jianhui Liu, Haoze Sun, Yanbing Zhang, Jiaxiu Jiang, Lin Song, Haoyang Huang, Nan Duan, Lei Zhu |
| date | 2026-09-03 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.03729 |
| affiliation | Hong Kong University of Science and Technology(GuangZhou) |
| upvotes | 9 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Despite the remarkable prowess of Vision-Language Models (VLMs) in general multimodal tasks, they remain fundamentally ``flat'' when reasoning about the physical world. We argue that this spatial bottleneck stems from a profound dimensional mismatch: while VLMs are trained to interpret 2D projections, true spatial reasoning demands the recovery of latent 3D geometry and temporal continuity. To conquer this high-dimensional complexity, we advocate a shift from monolithic learning to a ``divide and conquer'' paradigm. We present FactoSR, a factorized reinforcement learning framework that explicitly interpret the dimensions collapsed by visual projection. At its core, FactoSR decomposes the monolithic problem of world-consistent reasoning into three orthogonal, geometric sub-objectives: planar correspondence (XY), depth consistency (Z), and temporal reversibility (T). By optimizing these verifiable constraints within a unified policy learning mechanism, we effectively transform an ill-posed projection recovery problem into a series of tangible reasoning steps. Extensive evaluations on multi-view and video benchmarks demonstrate that this elegant decomposition yields substantial gains in 3D and 4D reasoning, achieving a 5.9% boost on VSI-Bench and 4.5% on All-Angles-Bench. Our findings suggest that reinforcing explicit, factorized 4D consistency is a critical step toward evolving VLMs into robust, world-aware reasoners. |
cite
@misc{yang2026unfold,
title={Unfold The World: Factorize 4D Properties in Reinforcing Spatial Reasoning},
author={Yijun Yang and Shenghe Zheng and Wenbo Li and Jianhui Liu and Haoze Sun and Yanbing Zhang and Jiaxiu Jiang and Lin Song and Haoyang Huang and Nan Duan and Lei Zhu},
year={2026},
eprint={2609.03729},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.03729}
}