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LEO-RobotAgent: A General-purpose Robotic Agent for Language-driven Embodied Operator

Lihuang Chen, Xiangyu Luo et al.Dec 11arXiv

LEO-RobotAgent is a simple but powerful framework that lets a language model think, plan, and operate many kinds of robots using natural language.

#LEO-RobotAgent#language-driven robotics#LLM agent

SWAA: Sliding Window Attention Adaptation for Efficient Long-Context LLMs Without Pretraining

Yijiong Yu, Jiale Liu et al.Dec 11arXiv

Long texts make standard attention in large language models very slow because it checks every word against every other word.

#Sliding Window Attention#SWAA#FA Decode

From Segments to Scenes: Temporal Understanding in Autonomous Driving via Vision-Language Model

Kevin Cannons, Saeed Ranjbar Alvar et al.Dec 4arXiv

This paper builds TAD, a brand-new test that checks if AI can understand what happens over time in real driving videos.

#Temporal understanding#Autonomous driving#Vision-language models

ReVSeg: Incentivizing the Reasoning Chain for Video Segmentation with Reinforcement Learning

Yifan Li, Yingda Yin et al.Dec 2arXiv

ReVSeg teaches an AI to segment objects in videos by thinking step-by-step instead of guessing everything at once.

#Reasoning Video Object Segmentation#Vision-Language Models#Temporal Grounding