Hey everyone,
I just went public with a new research paper/framework called the Spark Architecture. While most of us are focusing on quantizations and context windows, I’ve been looking at the "Motivation Gap."
The Spark is a persistent meta-logic layer that "bullies" the Reasoning Core into a state of constant self-interrogation. In this framework, the AI is given a browsing tool and a default motivation to resolve "Incompleteness."
How it handles skill acquisition: If the Spark identifies a goal it can’t solve, it realizes it needs a new "limb." It uses the Magnifier Scopes (targeted RAG) to study (e.g., learning C++), trains a LoRA in a separate sandbox, and plugs it into a Mixture-of-Experts bank.
The 8 Modules:
- Reasoning Core
- The Spark (Motivation Layer)
- Magnifier Scopes
- Autonomous Tool Creation (Discovery-based)
- Dual-Layer Memory
- Safe Self-Training
- MoE Bank
- Global Orchestrator
Repo: https://github.com/yassin123mom/the-spark-architecture.git