arXiv AI June 19, 2026 By Jinghan Yang, Yunchao Zhang, Wang Yuan, Haolun Wan, Jiaming Zhang, Zhengyang Hu, Yanchao Yang

Tri-Info: Generalizable, Interpretable Failure Prediction for VLA Models via Information Theory

arXiv:2606. 19998v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are increasingly deployed across diverse tasks, yet they remain black boxes whose physical interactions can cause irreversible harm, making generalizable and interpretable failure detection essential.

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

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

Aug 6

GUARD: Grounding Uncertainty and Ablation-Based Risk Detection for Diffusion-Based VLAs

arXiv:2608. 04510v1 Announce Type: cross Abstract: Diffusion-based vision-language-action (VLA) policies can generate plausible actions even when their predictions are weakly grounded in the visual and language evidence defining the task.

By Suhas Hegde, Jitendra Yasaswi Bharadwaj Katta

llms diffusion multimodal benchmarks safety

arXiv AI

Jul 3

Restoring Linguistic Grounding in VLA Models via Train-Free Attention Recalibration

arXiv:2603. 06001v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models enable robots to perform manipulation tasks directly from natural language instructions and are increasingly viewed as a foundation for generalist robotic policies.

By Ninghao Zhang, Bin Zhu, Shijie Zhou, Jingjing Chen

robotics multimodal benchmarks safety

arXiv AI

1d ago

FabriMAE I Trust Myself? Self-Evaluating VLA Action Generation with Markov Attention Entropy

arXiv:2608. 16697v1 Announce Type: new Abstract: Vision-Language-Action models (VLAs) integrate visual perception, language instruction, and action generation into end-to-end policies across heterogeneous architectures.

By Aniri, Chen Yilin, Jinhe Bi, Junfei Guo, Donglai Ran, Xu Bian, Zengjie Jin, Yujun Wang, Yijun Tian, Volker Tresp, Fei Shen, Tat-Seng Chua, Yunpu Ma

multimodal benchmarks

arXiv Machine Learning

Jun 11

Learning What to Say to Your VLA: Mostly Harmless Vision Language Action Model Steering

arXiv:2606. 12299v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models provide a natural language interface to robot control, but the mapping from language to behavior is often brittle and unintuitive: semantically similar instructions can induce drastically different behaviors, while some capabilities may not be elicitable through prompting alone.

By Hyun Joe Jeong, Gokul Swamy, Andrea Bajcsy

llms robotics fine-tuning multimodal

arXiv AI

Jun 30

Early Warning Signals for OpenVLA Failure under Visual Distribution Shift

arXiv:2606. 29699v1 Announce Type: cross Abstract: Vision Language Action models combine perception, language grounding, and control in a single policy, but their failures are hard to diagnose once visual conditions shift.

By Dipesh Tharu Mahato, Rachel Ren

robotics benchmarks

Related stories

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

GUARD: Grounding Uncertainty and Ablation-Based Risk Detection for Diffusion-Based VLAs

Restoring Linguistic Grounding in VLA Models via Train-Free Attention Recalibration

FabriMAE I Trust Myself? Self-Evaluating VLA Action Generation with Markov Attention Entropy

Learning What to Say to Your VLA: Mostly Harmless Vision Language Action Model Steering

Early Warning Signals for OpenVLA Failure under Visual Distribution Shift