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MemT-1.0: Towards Memory-Native Evolution

Zhiyu Li, Zeyu Zhang, Linyun Liu, Yiwei Bu, Chenyang Xi, Xichong Zhang, Yi Yu, Yu Yu, Zhichen Liu, Yuxiao Yi, Chunyu Li, Haoran Liu, Zhenkai Ding, Le Zhou, Juncheng Zhang, Boyu Chen, Zehao Lin, Guorui Li, Renyu Fu, Ji Li · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26825 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

为了实现这一目标,我们引入了 MemT-1.0,一个新的记忆基础模型架构,将记忆作为模型计算的一部分内化。建立在Metis上,MetT-1.0采用了Gated DeltaNet-2的内在能力,使它们能够持续积累和利用信息。 为此,我们进一步引入了MetT-1.0,一个新的记忆基础模型架构,将记忆作为模型计算的一部分内化。建立在Metis上,MetT-1.0采用了Gated DeltaNet-2的固态记忆更新机制,在每个转折中都具有稳定的代码级记忆更新机制。我们进一步引入了MetT-1.0和MetT-1.0的存储容量扩展的MetT-1.0的混合存储架构。经过有意的背骨准备,我们开发了一

Machine translation (Meta M2M100); the English original follows.

Abstract

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 and inference infrastructure. We release a model family comprising MemT-1.0-Nano and MemT-1.0-Flash, and conduct a comprehensive evaluation of memory and general capabilities. The results show that they outperform the parametric memory baselines on average and remain competitive with retrieval-augmented methods, while largely preserving general capabilities. At a history length of 128K tokens, MemT-1.0-Nano achieves approximately $1.86\times$ end-to-end inference speedup and a $66\times$ reduction in persistent storage relative to full context KV cache. By internalizing retrieved procedural experience, MemT-1.0 improves task success on agentic tasks, providing a practical step toward memory-native evolution.

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Topics: Computer science, Inference, Task (project management), Pipeline (software), Context (archaeology), Memory model

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