Zhengyang Huang

Multi-agent collaboration often performs worse than solo reasoning when peers hallucinate, but Bayesian tracking of advisor reliability rooted in internal belief states keeps collective performance strictly above autonomous baselines even under high misinformation.

Federated learning can achieve better accuracy-efficiency trade-offs under heterogeneous data by optimizing within a low-dimensional subspace and using a backfill-style update to retain residual components.