arXiv AI

MultihopSpatial: Multi-hop Compositional Spatial Reasoning Benchmark for Vision-Language Model

MultihopSpatial is a new benchmark for Vision‑Language Models that focuses on multi‑hop, compositional spatial reasoning with queries ranging from 1 to 3 hops across varied spatial perspectives. It introduces the Acc@50IoU metric, which jointly evaluates answer selection and precise bounding‑box prediction, and provides a large‑scale training corpus, MultihopSpatial‑Train, to improve spatial intelligence. Evaluation of 37 state‑of‑the‑art VLMs shows that compositional spatial reasoning remains a significant challenge, and reinforcement learning fine‑tuning on the corpus boosts both intrinsic spatial reasoning and downstream embodied manipulation performance.

arXiv AI

4d ago

MV-STRIDE: Enabling MLLMs to Master Multi-View Spatial Reasoning via Hierarchical Capability Modeling

MV-STRIDE is a Multi‑View hierarchical Spatial Reasoning dataset that models dependencies among perception, scene understanding, and contextual reasoning to support 3D spatial cognition. It introduces a QA generation pipeline that enforces cross‑view constraints, producing multi‑level reasoning tasks with chain‑of‑thought supervision. Experiments show that training on MV‑STRIDE yields state‑of‑the‑art performance on multi‑view spatial benchmarks, enabling MLLMs to reason robustly across diverse viewpoints.

By Jin Xu, Xiaojian Huang, Zhuodong Luo, Zhihong Zhang, Xin Liu, Jiansheng Wei, Xinzhi Wang, Jie Zhao, Xuejin Chen

llms multimodal benchmarks

arXiv AI

Jul 7

SpatialThinker: Reinforcing Scene Graph-Grounded Spatial Reasoning via Dense Rewards

arXiv:2511. 07403v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved remarkable progress in vision-language tasks, but continue to struggle with spatial reasoning.

By Hunar Batra, Haoqin Tu, Hardy Chen, Yuanze Lin, Cihang Xie, Ronald Clark

llms reinforcement-learning fine-tuning multimodal benchmarks

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.

By Yatai Ji, An-Chieh Cheng, Yang Fu, Yukang Chen, Han Zhang, Zhaojing Yang, Wei Huang, Ka Chun Cheung, Song Han, Vidya Nariyambut Murali, Pavlo Molchanov, Jan Kautz, Simon See, Hongxu Yin, Ping Luo, Sifei Liu

llms reinforcement-learning fine-tuning multimodal benchmarks safety

Hugging Face Trending Papers

Aug 12

SCOUT: Unlocking Enhanced Spatial Reasoning via Structured Chain-of-Thought and Multi-Objective Process Reward

Existing Vision-Language Models (VLMs) exhibits a critical bottleneck in robust spatial reasoning. Recent reinforcement learning (RL) methods aim to close this gap with verifiable outcomes, yet they suffer from poor credit assignment across intermediate reasoning steps.

llms reinforcement-learning multimodal benchmarks

arXiv AI

Aug 13

SCOUT: Unlocking Enhanced Spatial Reasoning via Structured Chain-of-Thought and Multi-Objective Process Reward

arXiv:2608. 12220v1 Announce Type: cross Abstract: Existing Vision-Language Models (VLMs) exhibits a critical bottleneck in robust spatial reasoning.

By Zile Zhou, Huining Yuan, Weichen Zhang, Xinlei Chen, Xiao-ping Zhang

llms reinforcement-learning multimodal benchmarks

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...

By Xinmiao Huang, Qisong He, Zhenglin Huang, Boxuan Wang, Zhuoyun Li, Guangliang Cheng, Yi Dong, Xiaowei Huang

llms agents robotics multimodal benchmarks

arXiv AI

Jun 12

Perceive, Interact, Reason: Building Tool-Augmented Visual Agents for Spatial Reasoning

arXiv:2606. 12830v1 Announce Type: cross Abstract: While recent vision-language models (VLMs) demonstrate strong multimodal understanding, they remain limited in spatial reasoning tasks that require active evidence acquisition and multi-step visual interaction.

By Changye Li, Meng Lu, Yi Wu, Ligeng Zhu

llms agents multimodal benchmarks

arXiv AI

Jun 11

SVoT: State-aware Visualization-of-Thought for Spatial Reasoning via Reinforcement Learning

arXiv:2606. 11770v1 Announce Type: new Abstract: Spatial reasoning remains a challenge for Multimodal Large Language Models (MLLMs), as it requires reliable multi-hop inference over both intermediate states and state transitions.

By Chao Lei, Yanbei Jiang, Markus Hiller, Zhijian Zhou, Xunye Tian, Krista A. Ehinger, Nir Lipovetzky

llms reinforcement-learning multimodal benchmarks

arXiv AI

Jun 12

SpatialClaw: Rethinking Action Interface for Agentic Spatial Reasoning

arXiv:2606. 13673v1 Announce Type: cross Abstract: Spatial reasoning, the ability to determine where objects are, how they relate, and how they move in 3D, remains a fundamental challenge for vision-language models (VLMs).

By Seokju Cho, Ryo Hachiuma, Abhishek Badki, Hang Su, Byung-Kwan Lee, Chan Hee Song, Sifei Liu, Subhashree Radhakrishnan, Seungryong Kim, Yu-Chiang Frank Wang, Min-Hung Chen

llms agents multimodal benchmarks

arXiv AI

Jun 16

Thinking with Visual Grounding

arXiv:2606. 16122v1 Announce Type: new Abstract: Visual thinking should not only sound right; it should show its evidence.

By Junkai Zhang, Yihe Deng, Kai-Wei Chang, Wei Wang

llms agents reinforcement-learning multimodal benchmarks

arXiv Computer Vision

Aug 25

Traj-VLN: Learning Pixel-Space Interaction via Autoregressive Trajectory Generation

arXiv:2607.10744v5 Announce Type: replace Abstract: Benefiting from the powerful priors embedded in large-scale pre-training data and the emerging commonsense reasoning ability, large language models...

By Changfei Fu, Guangcheng Chen, Aoxiang Gu, Haoxiang Liang, Wenjun Xu, Hong Zhang

llms rag agents robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 16

RoboPIN: Grounded Embodied Reasoning via Pinned Chain-of-Thought

arXiv:2606. 15753v1 Announce Type: new Abstract: Embodied reasoning requires models to perceive task-relevant objects and spaces in physical environments and maintain consistent visual grounding throughout multi-step reasoning.

By Yaoting Huang, Yifu Yuan, Linqi Han, Chengwen Li, Shuoheng Zhang, Xianze Yao, Hongyao Tang, Yan Zheng, Jianye Hao

llms robotics multimodal benchmarks safety

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