EMoE: Eigenbasis-Guided Routing for Mixture-of-Experts

2026-01-17

AI Summary

This work addresses two critical challenges in Mixture-of-Experts (MoE) architectures—expert load imbalance and functional homogenization—by proposing EMoE, a novel framework that learns an orthogonal eigenbasis from the input feature space. Tokens are projected onto this basis and routed according to their alignment with the principal components, thereby achieving a geometric partitioning of data. This approach introduces, for the first time, a principal component–aligned orthogonal routing mechanism that simultaneously ensures balanced expert utilization and functional diversity without relying on auxiliary balancing losses. Experimental results demonstrate that EMoE effectively mitigates expert overloading and representational redundancy, significantly enhancing model efficiency and promoting expert specialization.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The relentless scaling of deep learning models has led to unsustainable computational demands, positioning Mixture-of-Experts (MoE) architectures as a promising path towards greater efficiency. However, MoE models are plagued by two fundamental challenges: 1) a load imbalance problem known as the``rich get richer"phenomenon, where a few experts are over-utilized, and 2) an expert homogeneity problem, where experts learn redundant representations, negating their purpose. Current solutions typically employ an auxiliary load-balancing loss that, while mitigating imbalance, often exacerbates homogeneity by enforcing uniform routing at the expense of specialization. To resolve this, we introduce the Eigen-Mixture-of-Experts (EMoE), a novel architecture that leverages a routing mechanism based on a learned orthonormal eigenbasis. EMoE projects input tokens onto this shared eigenbasis and routes them based on their alignment with the principal components of the feature space. This principled, geometric partitioning of data intrinsically promotes both balanced expert utilization and the development of diverse, specialized experts, all without the need for a conflicting auxiliary loss function. Our code is publicly available at https://github.com/Belis0811/EMoE.

Problem

• Mixture-of-Experts
• load imbalance
• expert homogeneity
• rich get richer
• expert specialization

Innovation

• Mixture-of-Experts
• eigenbasis
• routing mechanism
• load balancing
• expert diversity