Introducing MemT-1.0, an open-weight model family with persistent, evolving memory built into model computation, enabling conversations and agents to reuse historical information without reloading the original context.
🤖 https://modelscope.ai/collections/MemTensor/MemT-10

🏆 Leads parametric memory methods: Across 11 memory benchmarks, MemT-1.0-Flash and MemT-1.0-Nano rank first and second among the evaluated methods.
🧠 Memory lives in the model: Dynamic local memory and trainable hyper memory retain past information without carrying the original conversation into every new inference.
⚡ Built for long-running agents: At 128K history, Nano delivers an official 1.86× end-to-end inference speedup and about 66× lower persistent storage than a full-context KV cache.
🛠️ Two backbones, one memory design: Nano is built on Qwen3.6-35B-A3B with about 3B active parameters; Flash builds on DeepSeek-V4-Flash with about 17B active parameters. Both support locally persistent memory sessions.
📜 Flash weights are released under MIT. Inherited backbone licenses still apply.