开源模型 MemT 1.0 发布，能跨对话复用记忆
一个叫 MemT 1.0 的开源模型，能记住你昨天的反馈并用在今天类似任务上，靠的是 MoS 多存储专家架构，和普通 MoE 不一样。
开源模型 MemT 1.0 可以把早期交互中的信息用在后续任务里，比如把用户昨天对类似任务的反馈用到今天的新任务上。它采用 Mixture-of-Storage（MoS）架构，由多个 memory expert 组成，并通过两个路由器控制信息的写入和读取。与传统 Mixture-of-Experts 不同，MemT 的每个 memory expert 每次都参与运算，只是权重不同。这样一来，模型无需访问原始对话，也能累积并复用此前交互中的信息。
This is pretty smart:
MemT 1.0 is a new open model that can use information from earlier interactions in later tasks.
For example, it can use the feedback you gave it yesterday on a similar task you are completing today.
The model uses a Mixture-of-Storage architecture (MoS) with multiple memory experts. Each expert maintains its own memory, and two routers control how it writes and retrieves information.
This is different from a traditional Mixture-of-Experts architecture. Every single memory expert in MemT's architecture participates every time, but with different weights.
The bottom line is that the model accumulates information from previous interactions and reuses it later without accessing the original conversation.
