arXiv AI July 10, 2026 By Ke Ren, Ali Salamatian, Kieran Pattison, Cyrus Neary

V-VLAPS: Value-Guided Planning for Vision-Language-Action Models

arXiv:2601. 00969v3 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models provide strong action priors for robotic manipulation, but their reactive behavior can fail under distribution shift and long-horizon task structure.

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 Computer Vision

3d ago

The Planning Limits of Latent World Models

arXiv:2609.39235v1 Announce Type: cross Abstract: World models offer a promising way to help robots understand how the physical world evolves and plan complex behaviours through imagination. Yet exis...

By Ali Alrasheed, Basim Azam, Naveed Akhtar

robotics multimodal

arXiv AI

Jul 1

Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models

arXiv:2606. 31846v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models offer a promising framework for robotic manipulation by connecting language instructions, visual observations, and continuous control.

By Lang Cao, Renhong Chen, Luyi Li, Peng Wang, Mofan Peng, Yitong Li

reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv Computer Vision

Aug 25

Guardian: Detecting Robotic Planning and Execution Errors with Vision-Language Models

arXiv:2512.01946v4 Announce Type: replace-cross Abstract: Robust robotic manipulation requires reliable failure detection and recovery. Although recent Vision-Language Models (VLMs) show promise in r...

By Paul Pacaud, Ricardo Garcia, Shizhe Chen, Cordelia Schmid

llms robotics multimodal benchmarks

arXiv AI

Jul 13

GATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning

arXiv:2607. 08894v1 Announce Type: new Abstract: Large Language Model (LLM) agents have shown promise in multi-step planning tasks, but existing approaches like LATS (Language Agent Tree Search) and ReAct rely heavily on LLM inference during planning, leading to high computational costs and stochastic behavior.

By Maureese Williams, Dymitr Nowicki

llms agents

Hugging Face Trending Papers

Jul 9

GATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning

Large Language Model (LLM) agents have shown promise in multi-step planning tasks, but existing approaches like LATS (Language Agent Tree Search) and ReAct rely heavily on LLM inference during planning, leading to high computational costs and stochastic behavior. We present \textbf{GATS} (Graph-Augmented Tree Search), a planning framework that combines systematic UCB1-based tree search with a layered world model to eliminate LLM calls during inference while achieving superior planning performance.

llms agents

arXiv Machine Learning

Jul 21

Foresight Residual RL for Long-Horizon Robot Manipulation with Vision-Language-Action Models

arXiv:2607. 16506v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies offer strong general-purpose manipulation priors, but often fail on tight-tolerance, contact-rich assembly due to long-horizon credit assignment and subtask coupling: a state that is geometrically successful for the current skill can be brittle for downstream skills.

By Yuhan Liu, Xinyu Zhang, Litao Liu, Abdeslam Boularias

reinforcement-learning robotics multimodal

One email a morning, machine-written

Related stories

The Planning Limits of Latent World Models

Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models

Guardian: Detecting Robotic Planning and Execution Errors with Vision-Language Models

GATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning

GATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning

Foresight Residual RL for Long-Horizon Robot Manipulation with Vision-Language-Action Models