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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

Japanese

メモリベースのステップベースの進化は、メモリをメモリコンテキストの一部として内部化するメモリベースの進化を必要とします。メモリベースの基礎モデルの固有の能力を構築し、情報を継続的に蓄積し、利用することを可能にします。この目的のために、メモリベースのメモリベースモデルの新しいアーキテクチャMentT-1.0を導入します。メモリコンテキストの一部としてメモリを内部化するメモリベースの新しいアーキテクチャMentT-1.0は、Gated DeltaNet-2を各ターン内で安定したトークンレベルメモリ更新メカニズムとサポートしています。メモリベースの容量をスケールするMentT-1.0

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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