I’m Kaiya (Ivy) Zhao and currently I am a first-year PhD student at MIT CSAIL, where I am fortunate to be co-advised by Phillip Isola and Josh Tenenbaum. Previously, I had a wonderful experience working with Guangyu Robert Yang at MIT on autonomous agents with real-time interaction.
My research interest lies in the intersection of AI and human intelligence, with a focus on AI systems that can understand and interact with humans in a natural manner. I am passionate about generalist agents and their learning capabilities within multi-agent setting and complex environment.
Specifically, I am curious about the following topics:
I publish under the name “Zhao Kaiya”, where “Zhao” is my surname and “Kaiya” is the forename. “Ivy”(ai·vee) is the name I usually go by.
Outside the research, my world is filled with the vibrancy of musicals, the companionship of cats and the thrill of travel. Each of these hobbies offers me a unique perspective on life and creativity.
Feel free to reach out to me via email or coffee chat. I am always open to new ideas and collaborations!
PhD in Computer Science, 2024 - present
Massachusetts Institute of Technology
BSc in Computer Science, 2020 - 2024
Fudan University
Highly autonomous generative agents powered by large language models promise to simulate intricate social behaviors in virtual societies. However, achieving real-time interactions with humans at a low computational cost remains challenging. Here, we introduce Lyfe Agents. They combine low-cost with real-time responsiveness, all while remaining intelligent and goal-oriented. Key innovations include: (1) an option-action framework, reducing the cost of high-level decisions; (2) asynchronous self-monitoring for better self-consistency; and (3) a Summarize-and-Forget memory mechanism, prioritizing critical memory items at a low cost. We evaluate Lyfe Agents’ self-motivation and sociability across several multi-agent scenarios in our custom LyfeGame 3D virtual environment platform. When equipped with our brain-inspired techniques, Lyfe Agents can exhibit human-like self-motivated social reasoning. For example, the agents can solve a crime (a murder mystery) through autonomous collaboration and information exchange. Meanwhile, our techniques enabled Lyfe Agents to operate at a computational cost 10-100 times lower than existing alternatives. Our findings underscore the transformative potential of autonomous generative agents to enrich human social experiences in virtual worlds.
Feel free to get in touch with me using the contact options below.