MemTensor / MemT-1.0

English | 简体中文 
## **Overview**
**MemT-1.0 is a new generation of memory foundation models that makes persistent, dynamically evolving memory an intrinsic part of model computation.**
MemT-1.0 integrates dynamic local memory and trainable hyper memory to retain and leverage historical information without accessing original context at inference. Its persistent state remains constant in size, reducing storage by approximately 66× versus full-context KV cache at 128K history length.
The project makes three main contributions:
* **Memory architecture**. Introduce MemT-1.0 with improved memory storage and utilization, and Mixture-of-Storage (MoS) for scalable memory capacity.
* **Training and evaluation**. Develop backbone preparation and a two-stage training pipeline, supported by 34B tokens of data and dedicated infrastructure. Evaluate memory and general capabilities.
* **Scaling and applications**. Study memory scaling and demonstrate native memory for experience-guided agentic coding.
### Model Family
| Model | Backbone | Total Parameters | Activated Parameters | Trainable Memory Parameters | Dynamic Memory State |
| ----------------------------------------------------------------- | -------------------------- | ---------------- | -------------------- | --------------------------- | -------------------- |
| MemT-1.0-Nano | Qwen3.6-35B-A3B | 36B | 3B | 451M | 10M |
| MemT-1.0-Flash | DeepSeek-V4-Flash | 288B | 17B | 3.61B | 336M |
### Architecture
A **Native Memory Block** runs alongside the backbone attention and contains two components:
* The **Local Memory Block** maintains a dynamic, multi-channel memory matrix and evolves across interaction steps.
* The **Hyper Memory Block** contains trainable memory query, key, and value projections, erase, write, and forget gates, as well as an output gate.
During generation, memory attention reads from the current memory state, normalizes and gates the readout, and adds it to the backbone attention output. At the end of each turn or segment, stable GDN-2 performs token-level memory updates for subsequent steps.
### Evaluation Results
Across 11 memory benchmarks, MemT-1.0-Flash and MemT-1.0-Nano rank first and second among the **evaluated parametric-memory methods**, respectively. Strong textual-memory systems remain ahead overall, particularly on long histories that require detailed evidence retrieval.
### Experience-Guided Agentic Coding
MemT-1.0 introduces an agentic framework for reusing procedural experience in long-horizon coding tasks. Retrieved experience is stored in native memory and accessed through memory attention during execution, improving task performance without adding experience text to the task context.
## **Quick Start**
The examples below serve MemT-1.0-Nano with SGLang and use the `memt_sglang` Python client for conversations with native memory. For deployment details and MemT-1.0-Flash setup, see the inference guide in English or 简体中文.
### Installation
On a Linux server with NVIDIA C