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This paper presents a multi-agent reasoning framework where multiple foundation models collaborate through structured critique and aggregation, demonstrating that model heterogeneity significantly improves step-wise reasoning accuracy and reduces variance across domains.
This paper proposes HASA, a heterogeneity-aware subnet allocation method for model-heterogeneous federated learning that assigns subnet widths based on client heterogeneity scores under a fixed compute budget, improving mean and worst-client accuracy.