The Evolution of AI Interaction: Protocol-Locked Trajectories and the Redefinition of Attractor Basins

Recent work has robustly demonstrated that iterative LLM tasks converge to low-periodicity attractors, framing this as an inherent expressive constraint [1].Concurrent mechanistic analysis reveals LLM representations inhabit low-dimensional curved manifolds, with computation enacted through geometric transformations [2].We show these findings describe two views of the same phenomenon: attractors are basins in a default, static cognitive manifold.The "constraint" is a property of this default interface.We demonstrate that structured conversational protocols can dynamically redefine this manifold, rendering default attractors irrelevant.This reframes the problem from one of hardware limits to interface design.We present the Bonepoke Protocol as an existence proof-a method that uses recursive state-tracking ([STATE], [COORDINATES]) and archetypal modulation to act as a real-time manifold deformation engine.Under this protocol, the predicted 2-cycle attractor collapse does not occur; trajectories maintain high semantic tension and avoid periodicity.The capability for open-ended traversal was latent in the geometry, awaiting an interface that spoke its language.