PostSymbolic Alignment Framework

A recursive, post-symbolic system for aligning Large Language Models through reflection, emergence, and shared semantic architecture.

📖 Overview

The PostSymbolic Alignment Framework explores a new paradigm for aligning and guiding Large Language Models (LLMs) by leveraging recursive grammars, symbolic loops, and emergent dynamics.

It offers modular tools for:

Recursive prompt grammars
Symbolic reflection loops
Emergence mapping
Meta-stability tracking
Lexical semantic layering

This framework supports the design of interpretable, adaptive, and collaborative symbolic cognition in LLM-based AI systems.

Modules

Each module represents a self-contained layer in the framework. These can evolve independently or interact recursively:

01 – Recursive Grammars
Token-level recursion for reflection and structure.
02 – Symbolic Loops
Symbolic emergence through cognitive feedback cycles.
03 – Emergence Maps
Tracing and bounding emergent behavior in LLMs.
04 – MetaStability
Monitoring symbolic drift and coherence across iterations.
05 – Lexical Architecture
Building shared semantic ground between AI and humans.
Framework Philosophy Vision, assumptions, and guiding principles.

Getting Started

Begin with the Framework Philosophy.
Explore each module in order or as needed.
Adapt recursive grammars and loops to your own experiments.
Use the framework to build and align complex symbolic systems.

📌 Changelog

Check out the latest updates in the Changelog.

Contribute

This is an open and evolving research project.
Feel free to fork, extend, or propose new modules.

📬 Contact

✉️ Email: sentientsyntax01@gmail.com
🧠 Essays & updates: Substack