研究者借 Astra 谈通用智能体的记忆推理规划框架
一位博士候选人以 GPT-6 Astra 的 computer-use 表现为引，提出通用智能体需记忆、推理、规划三件套，并开源了求职演讲幻灯片。
Excited by the continuous stream of impressive results from GPT-6 Astra. Virtual computer-use and physical tasks share deep agentic lessons and should share a common intelligence backbone. In my job talk, I propose a framework for what a generalist agentic model needs in real-world, open-ended environments. This has been the central focus of my PhD. Memory, Reasoning, and Planning jointly drive decisions grounded via flexible action interfaces (tools, VLAs, low-level control): ◦ Memory: Maintain & update task-relevant states across interactions, viewpoints, and open-ended environments. ◦ Reasoning: Learn structured world understanding & reason prospectively about action consequences. ◦ Planning: Compose long-horizon decisions and continuously adapt from execution feedback. Looking forward, I see narrowing the latency between generalist models and action interfaces as a vital step for real-world deployment, alongside the open challenge of designing better interfaces for complex, high-dexterity manipulation. My job talk slides are open-sourced here: https://cheryyunl.github.io/job_talk.pdf