arXiv AI August 25, 2026 By Xiwen Chen, Zelin Li, Zhiruo Zhou, Huiming Chen, Chenwei Wang, Xiaojun Zhu

Pointing-VLA: Typed Spatial Grounding Interfaces for Vision-Language-Action Manipulation

The Flow has not summarised this story yet — read it at arXiv AI.

arXiv Machine Learning

Jul 7

XS-VLA: Coupling Coarse-grained Spatial Distillation with Latent Flow Matching for Lightweight Robotic Control

arXiv:2607. 04171v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong multimodal understanding and spatial grounding, but their computational cost limits real-time robotic control.

By Lei Iok Tong, Qingchen Xie, Wei Huang, Ying Jie Yap, Yujie Zhang, Qianzhi Li, Xiaolong Liu, Zhidong Deng

llms diffusion robotics fine-tuning efficiency multimodal benchmarks

arXiv Machine Learning

Jun 8

RhinoVLA Technical Report

arXiv:2606. 07383v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for robotic manipulation, but real-time deployment on edge hardware remains challenging.

By Huixi Intelligence, :, Chen Zhang, Chenyang Zhou, Guanglei Ding, Guanghui He, Haibin Gao, Jiajia Chen, Jianyong Zhang, Lianyi Yu, Ningyi Xu, Ping Xu, Qingchen Li, Yingjun Hu, Yijia Zhang, Yuxi Liu

robotics fine-tuning multimodal

arXiv Computer Vision

2d ago

V-Link: Recovering Lost Visual Representations in Action DiT for Vision-Language-Action Models

arXiv:2608.25308v1 Announce Type: new Abstract: Vision-language-action (VLA) models provide a scalable path toward generalist robotic manipulation by integrating visual perception, language understan...

By Yehao Lu, Jiarui Yang, Yuning Su, Yufeng Xie, Yu Zhong, Yazhou Zhang, Haiyu Lan, Kaixiang Lu, Peiwen Lin, Chuang Wang, Zequn Qin, Enyu Li, Xi Li

robotics multimodal

arXiv Computer Vision

2d ago

Training-Free Interaction-Aligned Visual Token Pruning for Efficient Embodied Manipulation

The paper introduces Interaction‑Aligned Pruning (IAprune), a training‑free method for visual token pruning in embodied manipulation tasks. IAprune jointly decides per‑frame budget and token selection, using semantic‑motion spatial agreement to choose between conservative and aggressive coverage, and applies geometric residual correction to focus on under‑represented boundaries. Experiments on four policies, three simulation benchmarks, and a real‑robot platform show that IAprune matches unpruned performance on LIBERO while achieving up to 1.54× speed‑up and 1.48× acceleration on a real robot.

By Jintao Cheng, Weibin Li, Haozhe Wang, Gang Wang, Yipu Zhang, Xiaoyu Tang, Jin Wu, Xieyuanli Chen, Yunhui Liu, Wei Zhang

robotics efficiency multimodal benchmarks

arXiv AI

Aug 13

G0.5: One Autoregressive Stream for Robot Reasoning and Action

arXiv:2608. 11739v1 Announce Type: cross Abstract: The prevailing recipe for Vision-Language-Action (VLA) models couples a pretrained VLM with a separately trained flow-matching action expert.

By Yicheng Liu, Zibin Dong, Baijun Ye, Tianyuan Yuan, Tao Jiang, Anqi Yang, Shicheng Cao, Haonan Liu, Yue Sun, Zihan Guo, Xiao Liu, Dong Ke, Changxun Pan, Chenru Wu, Tailai Cheng, Xiaoshu Ren, Xinlei Zhang, Jianning Cui, Zijie Zhao, Haoyu Zhang, Kaiming Xu, Haodong Yang, Bowen Zhang, Jiahui Niu, Shaoting Zhu, Shiduo Zhang, Hang Zhao

llms nlp robotics fine-tuning multimodal benchmarks

arXiv AI

Jul 14

VIA: Visual Interface Agent for Robot Control

arXiv:2607. 11119v1 Announce Type: cross Abstract: Robot manipulation is a complex task that requires visual understanding, physical reasoning, planning, and closed-loop control.

By Hengyuan Hu, Priya Sundaresan, Jensen Gao, Dorsa Sadigh

llms agents robotics fine-tuning multimodal