MemT-1.0: Towards Memory-Native Evolution
Memory-native evolution requires making memory an intrinsic capability of foundation models, enabling them to continuously accumulate and utilize information. To this end, we introduce MemT-1.0, a new architecture of memory foundation models that internalize memory as part of model computation. Building on Metis, MemT-1.0 employs Gated DeltaNet-2 with a stable token-level memory update mechanism within each turn. We further introduce Mixture-of-Storage to scale native memory capacity. Following a deliberate backbone preparation, we develop a two-stage training pipeline consisting of continued pre-training and post-training, supported by 34B tokens of training data and efficient training a...
| Authors | Zhiyu Li, Zeyu Zhang, Linyun Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 19 October 2026 |
| Last updated at source | 20 October 2026 |
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