Overview
My research focuses on the transition from reactive to proactive AI. Most current LLM implementations are reactive—they wait for a prompt to act. I am building a framework that allows agents to initiate actions based on goals, internal time-states, and long-term memory.
Cognitive Layers
The architecture is divided into three main layers:
- Sensory/Short-term Buffer: Handles immediate context and real-time processing.
- Associative Memory: Links current context to historical data using vector embeddings.
- Execution/Action Controller: Decides when and how to act based on the synthesized state.
graph TD Goals[Internal Goals/Drives] --> Controller[Execution Controller] Inputs[Sensory Inputs] --> Buffer[Short-term Buffer] Buffer --> Memory[Associative Memory] Memory -->|Context Retrieval| Controller Controller -->|Proactive Action| UI[User Interaction / API Calling] Controller ---|Self-Update| Memory
Personal Research & Orchestration
This research serves as the foundation for my personal development of cognitive agents. I am currently building a modular framework where multiple AI agents work together on complex tasks through a centralized orchestrator, optimized for low-latency feedback loops.
Last updated on April 3, 2026 at 4:45 AM UTC+7. See Changelog