arXiv AI

PinpointQA: A Benchmark for Small Object-Centric Spatial Understanding in Indoor Videos

arXiv:2604. 08991v3 Announce Type: replace-cross Abstract: Reliable embodied interaction in indoor environments requires agents to precisely localize small everyday objects from visual observations.

arXiv AI

Jun 29

EXPLORE-Bench: Egocentric Scene Prediction with Long-Horizon Reasoning

arXiv:2603. 09731v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are increasingly considered as a foundation for embodied agents, yet it remains unclear whether they can reliably reason about the long-term physical consequences of actions from an egocentric viewpoint.

By Chengjun Yu, Xuhan Zhu, Chaoqun Du, Pengfei Yu, Wei Zhai, Yang Cao, Zheng-Jun Zha

llms agents robotics multimodal benchmarks

arXiv AI

Jul 7

Token-Based Affordance Grounding with Large Vision-Language Models

arXiv:2607. 03595v1 Announce Type: cross Abstract: Affordance grounding aims to localize image regions that support a specific action, serving as a core capability for physical intelligence and embodied perception.

By Seung Il Lee, Qinqian Lei, Daguang Xu, Dong Yang, Robby T. Tan, Yixin Chen, Bo Wang

llms robotics multimodal benchmarks

arXiv AI

Jun 17

m2sv: A Scalable Benchmark for Map-to-Street-View Spatial Reasoning

arXiv:2601. 19099v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) achieve strong performance on many multimodal benchmarks but remain brittle on spatial reasoning tasks that require aligning abstract overhead representations with egocentric views.

By Yosub Shin, Michael Buriek, Igor Molybog

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jul 1

PruneGround: Plug-and-play Spatial Pruning for 3D Visual Grounding

arXiv:2606. 31148v1 Announce Type: cross Abstract: 3D Visual Grounding (3DVG) aims to localize target objects in 3D scenes given natural language descriptions.

By Duc Cao Dinh, Khai Le-Duc, Florent Draye, Chris Ngo, Terry Jingchen Zhang, Bernhard Sch\"olkopf, Zhijing Jin

llms efficiency multimodal benchmarks

arXiv AI

Jul 2

MindEdit-Bench: Benchmarking Object-Level Counterfactual Spatial Reasoning in VLMs from In-the-Wild Photos

arXiv:2607. 00491v1 Announce Type: cross Abstract: Benchmarks for vision-language models (VLMs) mostly test observational spatial reasoning: models describe relations already visible in the input.

By Leyuan Yu, Xiao Tang, Minghao Liu, Xinyuan Li, Xiaokai Bai, Sheng Zhou, Qunshu Lin, Weihao Xuan, Naoto Yokoya

llms multimodal benchmarks

arXiv AI

Jun 6

Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models

arXiv:2606. 05833v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) excel at 2D semantic understanding but lack intrinsic 3D awareness, resulting in representations that fail to maintain geometric and spatial consistency across video frames.

By Haibo Wang, Lifu Huang

llms diffusion multimodal benchmarks

arXiv AI

Jun 6

LongSpace: Exploring Long-Horizon Spatial Memory from Perception to Recall in Video

arXiv:2606. 05677v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have advanced image and video understanding and can increasingly handle longer visual inputs.

By Shiqiang Lang, Jing Liu, Haoyang He, Peiwen Sun, Yuanteng Chen, Tao Liu, Lan Yang, Longteng Guo, Honggang Zhang

llms agents robotics multimodal benchmarks

arXiv AI

Jul 28

Spatial-IQ: Deconstructing Spatial Intelligence via Hierarchical Capability Tests

arXiv:2607. 22864v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) excel at visual interpretation but fail on spatial reasoning tasks that humans solve reliably.

By Patrick Rim, Tom Long, Ekta Prashnani, Ruth Rosenholtz, Ben Boudaoud, Peter Xenopoulos, Alex Wong, Joohwan Kim, Jae-Hyun Jung

llms reinforcement-learning multimodal benchmarks

arXiv AI

Jun 9

SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks

arXiv:2606. 09669v1 Announce Type: new Abstract: Spatial reasoning is a foundational capability for multimodal large language models (MLLMs) to perceive and operate within the physical world.

By Hongcheng Gao, Hailong Qu, Jingyi Tang, Jiahao Wang, Zihao Huang, Hengkang Qiao, Shihong Huang, Junming Yang, Yi Li, Hongyixuan Yuan, Wenjie Li, Bohan Zeng, Wenbo Li, Bo Wang, Jianhui Liu, Olive Huang, Haoyang Huang, Wentao Zhang, Guoqing Huang, Nan Duan, Yinpeng Dong

llms agents multimodal benchmarks

Hugging Face Trending Papers

Jun 4

Learning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) excel at 2D semantic understanding but lack intrinsic 3D awareness, resulting in representations that fail to maintain geometric and spatial consistency across video frames. Given the scarcity of large-scale 3D data, we present GeoVR, a novel framework that learns geometric representations using purely 2D video sequences.

llms diffusion multimodal benchmarks

arXiv AI

Jul 8

OpenGround: Planning-based Online Perception for Open-World 3D Visual Grounding

arXiv:2512. 23020v3 Announce Type: replace-cross Abstract: 3D visual grounding aims to locate objects based on natural language descriptions in 3D scenes.

By Wenyuan Huang, Zhenyu Zhang, Zhao Wang, Zhou Wei, Ting Huang, Fang Zhao, Jian Yang

llms benchmarks

arXiv AI

Jun 26

From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP

arXiv:2606. 26535v1 Announce Type: cross Abstract: Current VLM evaluations often conflate language priors with genuine spatial reasoning.

By Zhixing Li, Yinan Yu

multimodal safety

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