While large-scale language models (LLMs) have made significant progress in functionality, they still face several key limitations:
They rely primarily on offline corpora or fixed knowledge graphs and lack continuous awareness of real-time data and dynamic environments.
Models typically operate in a reactive manner. They generate outputs without tracking, evaluating, or closing the loop on their own thought processes.
Most systems cannot proactively predict contexts, schedule interactions, or manage emotional cues in complex human-machine or multi-task scenarios.
To address these issues, this draft proposes a multi-layer, scalable AGI activation mechanism that inserts several key modules between the “static data layer” and the “dynamic data layer” to achieve greater autonomy and adaptability in the future.
The proposed architecture is built on three primary layers plus an external activation/support mechanism:
Stores vast amounts of historical text, knowledge graphs, and offline training data.
Provides a stable memory base, essentially serving as a “knowledge repository” or “offline data lake.”
Connects to real-time data streams such as social media, sensor inputs, and environmental data.
Supplies a continuous, highly timely input that delivers “fresh signals” for model evolution and decision making.
The central processing unit that houses several key modules (detailed in Section 3).