arXiv Machine Learning August 10, 2026 By Yuewei Sun, Lang Qin, Zechuan Tian, Jingwen Li, Guiqin Wang, Shengzeng Huo, Wenxin Ren, Tao Fang, Xiaochen Zhang, Guanqing Deng, Xiang Wang, Xiaowen Dong, Qinghai Guo, Yuxin Ma

Fast and Accurate: An Adaptive VLA Inference Framework through Environment-aware Model Selection

Read the original on arXiv Machine Learning →

arXiv:2608. 06434v1 Announce Type: cross Abstract: Embodied intelligence demands both long-horizon reasoning and real-time closed-loop responsiveness.

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

arXiv AI

Jun 9

CT-VAM: A Cerebello-Thalamic-Inspired Vision-Action Model for Efficient Visuomotor Control

arXiv:2606. 09572v1 Announce Type: cross Abstract: Vision-language-action models have shown strong promise for robot manipulation, yet raw language is primarily needed to specify task intent rather than to be repeatedly processed during high-frequency low-level execution.

By Jiacheng Li, Yize Guo, Jiabin Guo, Qingchen Liu, Jiahu Qin

robotics efficiency multimodal

arXiv AI

1d ago

Algorithm-Architecture Co-Design for Efficient VLA Inference via Speculative Inference and Verification

arXiv:2608. 15636v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in the field of embodied AI, but their high computational cost and limited predicted action length hinder real-time deployment.

By Chunyu Qi, Zhuoran Song, Jian Weng, Haozhe Jiang, Xueyuan Liu, Naifeng Jing, Guanghui He, Xiaoyao Liang, Haibing Guan

robotics efficiency multimodal benchmarks

arXiv Machine Learning

Jun 5

Training One Model to Master Cross-Level Agentic Actions via Reinforcement Learning

arXiv:2512. 09706v2 Announce Type: replace Abstract: The paradigm of agentic AI is shifting from engineered complex workflows to post-training native models.

By Kaichen He, Zihao Wang, Muyao Li, Anji Liu, Yitao Liang

agents reinforcement-learning robotics fine-tuning benchmarks

arXiv AI

Aug 6

Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models

arXiv:2608. 04765v1 Announce Type: cross Abstract: Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control.

By Houze Xu, Jizhong Li, Ziyi Ye

llms nlp robotics fine-tuning multimodal

arXiv AI

Jul 28

VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference

arXiv:2512. 01031v2 Announce Type: replace-cross Abstract: Vision-Language-Action models (VLAs) are becoming increasingly capable across diverse robotic tasks.

By Jiaming Tang, Yufei Sun, Yilong Zhao, Shang Yang, Yujun Lin, Zhuoyang Zhang, James Hou, Yao Lu, Zhijian Liu, Song Han

robotics efficiency multimodal benchmarks

arXiv AI

Jun 9

AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing

arXiv:2606. 09811v1 Announce Type: cross Abstract: World-action models have emerged as a promising paradigm for robot manipulation, jointly modeling visual scene dynamics and actions to inject physical priors into policy learning.

By Jisong Cai, Long Ling, Shiwei Chu, Zhongshan Liu, Jiayue Kang, Zhixuan Liang, Wenjie Xu, Yinan Mao, Weinan Zhang, Xiaokang Yang, Ru Ying, Ran Zheng, Yao Mu

llms diffusion robotics benchmarks

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CT-VAM: A Cerebello-Thalamic-Inspired Vision-Action Model for Efficient Visuomotor Control

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AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing