arXiv AI July 28, 2026 By Roberto Spinelli, Thiago C. Martins

Embodied GPT-5.1: Evidence of a World Model?

arXiv:2607. 23899v1 Announce Type: cross Abstract: This exploratory study examines whether a large multimodal language model, GPT-5.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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

arXiv AI

Jun 11

Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models

arXiv:2606. 11324v1 Announce Type: cross Abstract: We introduce Embodied-R1.

By Yifu Yuan, Yaoting Huang, Xianze Yao, Yutong Li, Shuoheng Zhang, Linqi Han, Pengyi Li, Jiangeng Sun, Wenting Jia, Zhao Zhang, Yuhao Liu, Ruihao Liao, Yucheng Hu, Qiyu Wu, Yuxiao Li, Zibin Dong, Fei Ni, Yan Zheng, Shuyang Gu, Yi Ma, Hongyao Tang, Han Hu, Jianye Hao

llms reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv AI

Jul 7

Emergent Semantic Representations in World Models through Physical Interaction without Linguistic Supervision

arXiv:2605. 28865v2 Announce Type: replace-cross Abstract: What does a world model learn from physical exploration, without any linguistic supervision?

By Jiayi Fang

agents diffusion robotics safety

arXiv AI

Jul 1

Position: Vision-Language-Action Models Cannot Be Verified to Perform Physical Reasoning

arXiv:2606. 30686v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) systems, built on pretrained vision-language models (VLMs), have shown rapidly improving performance on robot manipulation benchmarks.

By Taozhao Chen, Ian Manchester, Huaming Chen

llms robotics multimodal benchmarks

arXiv AI

Jul 16

Grounded world models in biological organisms and future embodied AI

arXiv:2607. 13560v1 Announce Type: cross Abstract: Recent advances in generative and embodied AI have been driven by large-scale predictive learning over multimodal data.

By Giovanni Pezzulo, Davide Nuzzi, Marco D'Alessandro, Riccardo Proietti, Roberto Bottini, Paul Cisek

agents robotics multimodal

arXiv AI

Jun 6

World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis

arXiv:2606. 05979v1 Announce Type: cross Abstract: We propose world-language-action (WLA) models as a new class of embodied foundation models.

By Yi Yang, Zhihong Liu, Siqi Kou, Yiyang Chen, Yanzhe Hu, Jianbo Zhou, Boyuan Zhao, Zhijie Wei, Xiao Xia, Xueqi Li, Pengfei Liu, Zhijie Deng

llms diffusion robotics multimodal benchmarks

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

Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models

Emergent Semantic Representations in World Models through Physical Interaction without Linguistic Supervision

Position: Vision-Language-Action Models Cannot Be Verified to Perform Physical Reasoning

Grounded world models in biological organisms and future embodied AI

World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis