Attention-Aware Routing: Coupling Routing and Attention in MoEs
In Mixture-of-Experts language models, the router typically selects and weights experts based on the token's hidden state, utilizing limited contextual information. We propose Attention-Aware Routing (AAR), which augments the router with temporal and spectral features extracted from a sliding window of attention weights that represent a summary of the model's contextual state, disentangled from the hidden state. Keeping the base transformer entirely frozen, we train only the routing parameters, isolating routing as the sole variable. AAR improves GSM8K by +3.37 pp over a routing-only SFT baseline on OLMoE. Beyond performance, we show that routing and attention form a coupled circuit: rout...
| Authors | Despoina Kosmopoulou, Anastasios Tsetsilas, Efthymios Georgiou et al. |
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| Primary category | cs.AI |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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