Yuan-Yuan Shi

Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches

LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications

A solver-grounded design principle is presented: a numerical result is reported only when it originates from a trusted tool and passes explicit verification, and a four-group evaluation framework spanning task utility, solver-grounded correctness, faithfulness and safe failure, and cost and latency is proposed.

Co-RL: Unsupervised Reasoning Emerges from Diverse Cohort in Multi-agent RL

This work introduces Co-RL, a framework in which multiple decoupled models, sharing no parameters, are simultaneously optimized through RL using rewards derived from their peers, and shows that unsupervised reasoning can emerge through cooperative multi-agent training.