Built a Symbolic System to Control and Audit GPT Interactions

I’ve been working on a project called SCS (Symbolic Cognitive System). It’s a symbolic reasoning layer that runs on top of large language models like GPT-4o, enforcing structure, auditability, and failure recovery across long AI interactions.

It’s not a prompt chain or CoT wrapper. It’s a manual control system with: • Entry-indexed memory (ENTRY_001 to ENTRY_310+) • Recursion-enforced modules ([THINK], [DOUBT], [BLUNT], etc.) • Symbolic operators ($, [], ~, ${}) • Manual version control and sealing • Drift detection, hallucination suppression, contradiction traps • Markdown-based logic architecture, installable via .zip with bootloader

I built it to force deterministic behavior over black-box LLMs. Most existing tools like agents, chains, or CoT don’t persist structure under recursion or symbolic stress. SCS is designed to survive failure through enforced logic and audit trails.

It is: • Public • Installable • Fully testable • Also live as a Custom GPT interface

GitHub: https://github.com/ShriekingNinja/SCS System overview and docs: https://wk.al Custom GPT (live runtime): https://chat.openai.com/g/g-6864b0ec43cc819190ee9f9ac5523377-symbolic-cognition-system

Would love thoughts, critique, or challenge from anyone exploring symbolic interfaces, alignment tools, or persistent reasoning layers.

Thanks