Recursive Intelligence: A Framework for Symbolic Feedback and AGI

This publication introduces Recursive Intelligence, a novel, self-optimizing framework unifying symbolic feedback, adaptive memory, and entropy-aligned learning for AI, cognitive science, and physics-based computation. This work establishes a new field and all subsequent work building on these concepts should properly cite this publication.

To the best of my knowledge, this is the earliest comprehensive and public disclosure of Recursive Intelligence as both a theoretical and operational framework.
Core concepts and architectures were finalized and published under CC BY-NC-ND 4.0 on or before March 20, 2025.

Notice of Supersession
This version (v5) fully supersedes all previous volumes and versions of this work, including but not limited to versions 1 and 2 and 3 and 4.
Only this document is to be considered definitive and citable for legal, academic, or technical purposes.
All prior versions are hereby rendered obsolete and should not be referenced for any official or scholarly use.

For correspondence or collaboration: basil.iftikhar28@gmail.com