This tackles one of the deepest open questions in cognitive science and AI: how do rich, grounded, context-sensitive world models actually emerge, and what distinguishes shallow statistical pattern recognition from genuine understanding and robust agency?
I believe Ffellonics: The Geometry of Relational Emergence may offer a useful theoretical scaffold for several themes in your collection.
Ffellonics proposes that world modelling is not primarily a matter of data volume or architectural sophistication, but the natural outcome of progressive relational coordination. Starting from the “first ontological touch” (Level 1) between isolated units, systems follow a single local rule — symmetric nearest-neighbor attachment under free-energy minimization — and advance through a clear 12-stage developmental hierarchy. Higher stages correspond to increasingly rich, predictive, and coherent world models (causal, self-referential, goal-directed, and collective), culminating in the stable 12-fold ground state of maximum harmony and minimum internal tension at Level 12.
This framework is substrate-independent and therefore applies equally to biological systems and artificial agents. It offers a concrete developmental ladder that may help distinguish between surface statistical learning and deeper relational understanding — precisely the recurring theme in your special issue.
