Intelligence as an adaptive boundary process.
AIF formalizes intelligence as a boundary regulated adaptive process. Grounded in SymC principles, it studies how systems maintain coherence under uncertainty through constraint negotiation, regulation, and phase transitions. It focuses on adaptive stability rather than benchmark optimization or ML scaling.
This repository hosts the AIF manuscripts and supporting methodology material for the SymC research program, plus future reference implementations and methods as they are added.
AIF is not a machine learning library, not a benchmark suite, and not a scaling proposal for mainstream AI.
- AIF v4 (PDF):
SymC_AIFv4.pdf - SymC Methodology (PDF):
SymC_Methodology.pdf - Archive bundle:
SymC_AIFv3v4.zip
AIF centers on:
- Adaptive stability at the critical boundary
- Constraint negotiation and regulation
- Coherence under stress and uncertainty
- Transition dynamics between rigid, chaotic, and adaptive regimes
Current contents are manuscript first. Code modules and reproducible experiments will be added as the methods layer is formalized.
If you use AIF in academic work, please cite the corresponding Zenodo record when available.
If you are referencing the framework generally, cite this repository and the SymC Foundations repository.
- Foundations: https://github.com/SymCUniverse/Foundations
- Biomedical: https://github.com/SymCUniverse/Biomedical
Nate Christensen
natechristensen@SymCUniverse@gmail.com