How can #Physical# #AI# systems maintain memory, reason over past observations, and use that information to make better decisions? Memory is an emerging research direction for Physical AI, where agents must operate under partial observability and make decisions informed by the history of their interactions with the world. In our new paper, Vision-Language-Action Autonomous Driving Agent with Language-based Memory, we introduce AD-Memo, a driving agent that uses language-based memory to capture decision-relevant information about surrounding objects and leverage it in subsequent reasoning and driving decisions. This builds on our earlier work, Planning-aligned Token Compression for Long-Context Autonomous Driving, where we explored how to selectively preserve historical information by aligning memory compression with driving intent and planning objectives. Together, these works explore an important direction for Physical AI: building agents that learn what information to retain, how to represent it, and how to use it to make better decisions over time. 📄 Language-based memory paper: (project webpage: 📄 Planning-aligned token compression paper: Kudos to all the authors, especially the lead authors, Kai Yan and Zhixuan Liang, and their mentors, @WenjieLuo and @Yuxiao_Chen_. #PhysicalAI# #AutonomousDriving# #Robotics# #VLA# @NVIDIADRIVE @NVIDIAAI