Toward Self-Evolving Agent Societies: Infrastructure, Coordination, and Verifiable Improvement.
My research centers on a fundamental transition now underway: intelligence is no longer confined to isolated models or tools, but increasingly embodied in agents that act, learn, and influence one another within shared environments.
I frame this direction as Societies of Agents - systems in which multiple learning agents form structured, adaptive, and value-driven societies rather than merely coexisting as independent components.
This research agenda unfolds across three interconnected layers:
I develop training and execution frameworks that enable long-horizon learning, coordination, and adaptation among agents. This layer focuses on how agents are systematically organized to support sustained interaction and collective behavior.
Building on this infrastructure, I study how datasets, post-training, reward shaping, and workflow design give rise to stable, interpretable, and transferable agent behaviors. The central question here is not model capability, but how agents acquire roles, responsibilities, and norms within a society.
At the highest level, I explore how societies of agents operate in industrial and real-world settings, where cooperation and competition coexist, and where governance, credit assignment, and value flow become central design challenges.
Across reinforcement learning, multi-agent systems, trust modeling, and governance, my work is guided by a single conviction: the future of intelligence lies not in isolated agents, but in learning societies of agents.
For collaboration opportunities or more information about my research, please contact me.