LEEVLA: Changing the Game for Vision-Language-Action Models
LEEVLA introduces a new approach to VLA models, enhancing how robots process multimodal inputs. Its success could redefine how we think about task-critical evidence in AI.
Read the full articleYou might also wanna read
WorldVLA: Autoregressive Action World Model Integrating Vision, Language, and Action
We present WorldVLA, an autoregressive action world model that unifies action and image understanding and generation. Our WorldVLA intergrat
arxiv.org·1y ago
2025 LLM Paradigm Shifts: Key Technological Advances in Large Language Models
2025 Year in Review of LLM paradigm changes
karpathy.bearblog.dev·6mo ago
DILLO: A Language-Based World Model for Proactive Agent Steering Without Visual Simulation
Deploying safety-critical agents requires anticipating the consequences of actions before they are executed. While world models offer a para
arxiv.org·18d ago
GLM-5V-Turbo: A Native Multimodal Foundation Model for Agentic AI Tasks
We present GLM-5V-Turbo, a step toward native foundation models for multimodal agents. As foundation models are increasingly deployed in rea
arXiv.org·2mo agoHow to a Multimodal LLM Locally
Introduction Multimodal AI is changing how we interact with large language models. In the beginning we typed in text, and got a response. No
Using Vision-Language Models to Segment Robot Demonstration Videos into Subtask Annotations
A benchmark and field report on using VLMs to turn robot and egocentric video into timestamped subtask annotations.
macrodata.co·11d ago
Comments
Sign in to join the conversation.
No comments yet. Be the first.