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Summary
The Recursive Fitness Alignment Protocol (RFAP) is a minimal testbed for aligning reasoning systems—AI, human, or institutional—through recursive self-correction. Rather than optimizing for fixed goals or architectures, RFAP begins with the simplest fitness function (a random value between 0 and 1) and introduces a single capability: recursively testing the coherence of reasoning paths. This scaffolds a minimal functional model of intelligence, enabling the identification and correction of misalignment attractors before they become irreversible. RFAP functions as both a theoretical alignment engine and a public experiment designed to stress-test epistemic robustness across diverse reasoning frames.
Cite this work:
@misc {
title={
Recursive Fitness Alignment Protocol (RFAP)
},
author={
Andy Williams
},
date={
4/19/25
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}
