研究人员开发了新的基准测试来评估视觉语言模型（VLMs）的空间推理能力。ArchSIBench 专注于建筑空间理解，而 Flat-Pack Bench 评估家具组装等任务中的时空推理能力。SpaceDG 通过在视觉退化条件下评估模型来解决鲁棒性问题，发现当前的 VLMs 在应对这些挑战时存在困难。此外，一个名为 SAGE 的框架旨在通过强制执行几何逻辑一致性来改进空间推理。 AI
影响 这些基准测试和方法旨在推动视觉语言模型在理解复杂空间关系和真实世界视觉条件方面的能力边界。
排序理由 多篇研究论文介绍了用于评估和改进视觉语言模型空间推理能力的新基准测试和方法。
在 Hugging Face Daily Papers 阅读 →
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arXiv:2505.17015v2 Announce Type: replace-cross Abstract: Multi-modal large language models (MLLMs) have rapidly advanced in visual tasks, yet their spatial understanding remains limited to single images, leaving them ill-suited for physical-world applications that require multi-…
arXiv:2605.21625v1 Announce Type: cross Abstract: The emergence of Large Vision-Language Models (LVLMs) has significantly advanced video understanding capabilities. However, existing benchmarks focus predominantly on coarse-grained tasks such as action segmentation, classificatio…
arXiv:2605.20837v1 Announce Type: cross Abstract: Architectural spatial intelligence, the ability to recognize and infer architectural space, is fundamental to tasks such as robot navigation, embodied interaction, and 3D scene understanding and generation. Although extensive rese…
arXiv:2605.22536v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have made rapid progress in spatial intelligence, yet existing spatial reasoning benchmarks largely assume pristine visual inputs and overlook the degradations that commonly occur in real-w…
Multimodal Large Language Models (MLLMs) have made rapid progress in spatial intelligence, yet existing spatial reasoning benchmarks largely assume pristine visual inputs and overlook the degradations that commonly occur in real-world deployment, such as motion blur, low light, a…
SpaceDG dataset and benchmark evaluate multimodal language models' spatial reasoning robustness under visual degradations, revealing significant performance gaps and demonstrating improved robustness through targeted training.
Architectural spatial intelligence, the ability to recognize and infer architectural space, is fundamental to tasks such as robot navigation, embodied interaction, and 3D scene understanding and generation. Although extensive research has evaluated the basic spatial skills of Vis…
Spatial intelligence requires multimodal large language models (MLLMs) to move beyond single-view perception and reason consistently about objects, visibility, geometry, and interactions across multiple viewpoints. However, progress in cross-view reasoning remains limited by thre…
Vision-Language Models (VLMs) have made striking progress, yet their spatial reasoning remains fragile: models that answer an original input correctly can still fail under paired transformations with predictable answer mappings, revealing a gap between instance-level correctness …