arXiv AI October 15, 2026 By Jun Xiao, Rui Ye, Dong Cao, Jinyu Gu, Ying Wang, Zhiliang Zhu

VLA-GAUGE: Systematically Evaluating Generalization and Robustness in Vision-Language-Action Models

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

arXiv AI

Jul 21

RobustVLA: On Robustness of Vision-Language-Action Model against Multi-Modal Perturbations

arXiv:2510. 00037v5 Announce Type: replace-cross Abstract: In Vision-Language-Actionf(VLA) models, robustness to real-world perturbations is critical for deployment.

By Jianing Guo, Zhenhong Wu, Chang Tu, Yiyao Ma, Xiangqi Kong, Zhiqian Liu, Jiaming Ji, Shuning Zhang, Yuanpei Chen, Kai Chen, Qi Dou, Yaodong Yang, Xianglong Liu, Huijie Zhao, Weifeng Lv, Simin Li

llms diffusion reinforcement-learning robotics multimodal safety

arXiv Machine Learning

Oct 8

Rephrase Before You Act: Characterizing and Mitigating Language Sensitivity in Vision-Language-Action Models

arXiv:2610.10526v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) are strikingly sensitive to instruction phrasing and do not inherit the language robustness of the vision-langua...

By Mikey Watts (Independent Researcher), Yuchen Cui (University of California, Los Angeles)

llms multimodal safety

arXiv Machine Learning

Sep 17

Not All Layers Need Tuning: Diagnosing and Directing Adaptation in Vision-Language-Action Models

arXiv:2609.18084v1 Announce Type: cross Abstract: Fine-tuning a Vision-Language-Action (VLA) model for a new deployment environment is expensive, yet most methods apply uniform-capacity adapters to e...

By Shahram Najam Syed, Arthur Jakobsson, Prayuj Sachdev, Jeffrey Ichnowski

fine-tuning multimodal safety

arXiv Machine Learning

Sep 15

Part Grounding, Not Action Knowledge: Locating the Bottleneck in VLM Affordance Prediction

arXiv:2609.13225v1 Announce Type: cross Abstract: Benchmarks agree that vision-language models reason poorly about low-level manipulation, but an aggregate accuracy score does not say which step fail...

By Sarthak Sattigeri

llms robotics multimodal benchmarks

arXiv AI

Jul 13

CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding

arXiv:2607. 08974v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) inherit semantic capabilities from pretrained VLMs, yet large-scale post-training on robot data and architectural modifications can reshape the backbone so extensively that it becomes difficult to isolate what the VLM contributes to control.

By Yuri Ishitoya, Jeremy Siburian, Masashi Hamaya, Kuniaki Saito, Cristian C. Beltran-Hernandez, Mai Nishimura

robotics fine-tuning multimodal

arXiv AI

Jun 2

VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models

arXiv:2601. 03309v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models, which integrate pretrained large Vision-Language Models (VLM) into their policy backbone, are gaining significant attention for their promising generalization capabilities.

By Jianke Zhang, Xiaoyu Chen, Qiuyue Wang, Mingsheng Li, Yanjiang Guo, Yucheng Hu, Jiajun Zhang, Shuai Bai, Junyang Lin, Jianyu Chen

llms computer-vision robotics fine-tuning multimodal benchmarks

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Related stories

RobustVLA: On Robustness of Vision-Language-Action Model against Multi-Modal Perturbations

Rephrase Before You Act: Characterizing and Mitigating Language Sensitivity in Vision-Language-Action Models

Not All Layers Need Tuning: Diagnosing and Directing Adaptation in Vision-Language-Action Models

Part Grounding, Not Action Knowledge: Locating the Bottleneck in VLM Affordance Prediction

CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding

VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models