MixFormer: Linear Transformer with Mixture of Memory Experts
State Space Models (SSMs), as a mainstream research direction of linear Transformers, aim to achieve higher efficiency than standard Transformers in long-context modeling. However, existing SSMs suffer from limited input adaptivity and constrained memory capacity, leading to information loss when modeling ultra-long sequences. To address these limitations, we propose MixFormer, a novel linear Transformer that integrates a Mixture-of-Memory-Experts (MoE) mechanism. Specifically, the model maintains differentiated memory states through multiple collaborating memory experts and employs a novel Time-Aware Linear Attention (TALA) mechanism, which leverages learnable exponential decay functions...
| Authors | Yu Guo, Lei Duan |
|---|---|
| Primary category | cs.LG |
| Published | 10 August 2026 |
| Last updated at source | 10 August 2026 |
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