Hugging Face Trending Papers
DynThinker: Unifying Static 3D and Dynamic 4D Spatial Reasoning
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arXiv Computer Vision
4d ago
DynThinker: Unifying Static 3D and Dynamic 4D Spatial Reasoning
arXiv:2610.20598v1 Announce Type: new Abstract: Achieving general spatial intelligence requires a unified understanding of both static 3D and dynamic 4D. However, we find that existing models struggl...
arXiv Computer Vision
Aug 26
Spatial-DISE: A Unified Benchmark for Evaluating Spatial Reasoning in Vision-Language Models
arXiv:2510.13394v4 Announce Type: replace Abstract: Spatial reasoning ability is crucial for Vision Language Models (VLMs) to support real-world applications in diverse domains including robotics, au...
arXiv Computer Vision
4d ago
GeoThinker-$F$: Endogenous Predictive World Representations for Fast Spatial Intelligence
arXiv:2610.20592v1 Announce Type: new Abstract: Existing geometry-aware Multimodal Large Language Models (MLLMs) increasingly rely on external 3D encoders to provide structural priors, but executing...
arXiv AI
Jun 17
Reinforcing Dual-Path Reasoning in Spatial Vision Language Models
arXiv:2606. 17539v1 Announce Type: cross Abstract: Spatial VLMs have made substantial progress in geometric perception, yet complex spatial reasoning requiring multi-step inference over depth, distance, and scene relations remains challenging.
arXiv AI
Jul 9
SpaR3D-MoE: Adaptive 3D Spatial Reasoning from Sparse Views Meets Geometry-Inductive Mixture-of-Experts
arXiv:2607. 06620v1 Announce Type: cross Abstract: Recent Multimodal Large Language Models (MLLMs) struggle to bridge the representational gap between 2D semantic understanding and 3D spatial geometry.
arXiv Computer Vision
Sep 4
Unfold The World: Factorize 4D Properties in Reinforcing Spatial Reasoning
The paper introduces FactoSR, a factorized reinforcement learning framework designed to improve spatial reasoning in Vision‑Language Models by addressing a dimensional mismatch between 2D visual inputs and the 3D+temporal nature of the physical world. FactoSR decomposes the reasoning task into three orthogonal geometric sub‑objectives—planar correspondence (XY), depth consistency (Z), and temporal reversibility (T)—and optimizes these constraints within a unified policy learning mechanism. Experiments on multi‑view and video benchmarks show that this decomposition yields significant performance gains, achieving a 5.9% improvement on VSI‑Bench and 4.5% on All‑Angles‑Bench.