Yuanhong Wu

Architecturally diverse quantum-classical models fused via Combinatorial Fusion Analysis consistently outperform individual models and literature baselines, unlocking significant accuracy gains on classification tasks.

This work model agents as both arbitrage extractors and Maximal Extractable Value targets, derive the optimal trade size for a risk-averse agent under mean-variance utility with stochastic bridge delays, and formalize multi-chain path selection as a belief-weighted online learning problem whose belief estimates converge under a Robbins-Monro schedule.

LLMs can be better aligned to human values by fusing the outputs of multiple "moral agents" representing diverse ethical perspectives, outperforming single-agent approaches.