arXiv Machine Learning
RACER: Role-Aligned Competence Estimation for Human-AI Routing
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From Task Mixtures to Specialized Experts
The paper investigates federated learning where each client’s data consists of unknown mixtures of distinct tasks, a scenario termed compound heterogeneity. It shows that when tasks share a common feature geometry, the optimal model for a mixed client is a convex combination of task‑specific models, motivating input‑dependent routing to specialized experts. The authors propose FedSEE, a method that recovers task experts via a convex program and achieves better performance than baselines, reducing negative transfer by 2.9 points overall and 3.7 points for the worst‑served quartile.
arXiv AI
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Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning
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ProbMoE: Differentiable Probabilistic Routing for Mixture-of-Experts
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Towards a Statistical Understanding of Mixture-of-Experts
The paper presents a statistical framework for Mixture-of-Experts (MoE) models, treating them as localized aggregation systems. It derives oracle risk bounds that separate approximation, expert‑learning, and router‑estimation errors for both dense and sparse routing with evolving experts. The authors also analyze how sparse Top‑K routing balances computational cost with performance, interpret gating geometrically, and explain how shared experts can capture common predictive structure while allowing routed experts to focus on local residuals.
arXiv AI
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Skill-Based Mixture-of-Experts: Adaptive Routing for Heterogeneous Reasoning via Inferred Skills
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arXiv AI
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MoRSE: Task-Oriented Multi-Agent System with Mixture of Role-Subtask Experts
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