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cs.LG2026
When Do Multi-Agent Systems Help? An Information Bottleneck Perspective
Wendi Yu, Lianhao Zhou, Xiangjue Dong +6
LLM powered multi-agent systems (MAS) have emerged as a promising paradigm for complex tasks. However, their advantages over single-agent systems (SAS) remain unclear, with perform…
cs.LG2026
Perron--Frobenius Operator Matching for Generative Modeling
Shiqi Zhang, Wuwei Wu, Jaemin Oh +2
We introduce Perron--Frobenius Operator Matching (PFOM), a generative framework that matches density evolution via the integral PF operator, subsuming flow, diffusion, and jump mod…
cs.LG2026
Goal-driven Bayesian Optimal Experimental Design for Robust Decision-Making Under Model Uncertainty
Jinwoo Go, Xiaoning Qian, Byung-Jun Yoon
Bayesian optimal experimental design (BOED) selects experiments to maximize information gain about model parameters. However, in decision-critical settings, reducing parameter unce…