This project presents an applied example of the Semantic Logic System (SLS) framework, designed and published by Vincent Shing Hin Chong.
The Semantic Stable Agent enables a lightweight, modular, and systematic way to process user tasks by dynamically activating five semantic reasoning layers.
It serves as an experimental demonstration of how language-native recursive semantic control can operate without memory or plugins, purely through structured prompts.
A semantic directive core and:
Every time the directive core receives a new input, it activates the following layers sequentially:
- Layer 1: Task Initialization
- Layer 2: Goal Refinement
- Layer 3: Reasoning and Expansion
- Layer 4: Semantic Inspection and Correction
- Layer 5: Conclusion Synthesis
If inconsistency is detected during inspection, the system autonomously returns to Layer 1 to re-analyze the task.
After completing Layer 5 successfully, the Directive Core is re-activated, waiting for the next user input.
This structure follows the core belief of SLS:
Language is not only communication but the architecture of cognition.
By constructing semantic modular flows, even a basic LLM agent can maintain internal logic, correction capability, and structural resilience.
This project is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).
You are free to use, modify, and distribute this work with proper attribution to the original author.