arXiv AI June 12, 2026 By Hyeonbeom Choi, Daechul Ahn, Youhan Lee, Taewook Kang, Seongwon Cho, Jonghyun Choi

SCALE: Self-uncertainty Conditioned Adaptive Looking and Execution for Vision-Language-Action Models

arXiv:2602. 04208v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robotic control, with test-time scaling (TTS) gaining attention to enhance robustness beyond training.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 7

Verifier-free Test-Time Sampling for Vision-Language-Action Models

arXiv:2510. 05681v2 Announce Type: replace-cross Abstract: Vision-Language-Action models (VLAs) have demonstrated remarkable performance in robot control.

By Suhyeok Jang, Dongyoung Kim, Changyeon Kim, Youngsuk Kim, Jinwoo Shin

robotics 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

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 Computer Vision

3d ago

Correcting WHERE, Preserving HOW: Compositional Generalization for Vision-Language-Action Models via Referential Guidance

arXiv:2609.38616v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models enable flexible action generation, their generalization across diverse environmental elements, including ma...

By Yanyan Zhang, Disheng Liu, Xinpeng Li, Chaoda Song, Mohsen Hariri, Debargha Ganguly, Wang Yang, Kai Ye, Bryce Grant, Vipin Chaudhary, Yu Yin

robotics multimodal

arXiv Computer Vision

Sep 3

Towards Zero-Shot Transfer Across Embodiments For Driving VLAs

The paper investigates how Vision‑Language‑Action (VLA) models can generalise across different driving environments and camera setups. It introduces a multi‑dataset training strategy and an auxiliary objective called BEV‑Forcing, which injects bird‑eye‑view spatial information into the VLA backbone to improve both in‑distribution and out‑of‑distribution performance on a limited number of camera rigs. The authors observe that while BEV‑Forcing helps when training data is scarce, its advantage diminishes as the number of training embodiments grows, suggesting that scaling diversity may reduce the impact of such auxiliary tasks.

By Caio Azevedo, Stefano Sabatini, Sascha Hornauer, Fabien Moutarde

agents robotics fine-tuning multimodal

arXiv Machine Learning

Jun 10

Task Robustness via Re-Labelling Vision-Action Robot Data

arXiv:2606. 10918v1 Announce Type: cross Abstract: The recent trend in scaling models for robot learning has resulted in impressive policies that can perform various manipulation tasks and generalize to novel scenarios.

By Artur Kuramshin, \"Ozg\"ur Aslan, Cyrus Neary, Glen Berseth

llms robotics multimodal