Research
The Intelligence Ratchet: A Theoretical Framework for Self-Stabilizing Artificial Superintelligence
Abstract
Current paradigms in Artificial Intelligence face a seemingly intractable trade-off between Capability and Safety. Systems capable of recursive self-improvement (the "Singularity") risk unbounded instability, while systems with provable safety guarantees ("Bounded Optimality") are mathematically prevented from generating novel solutions. This paper introduces the General Interaction Dynamics Engine (GIDE), a theoretical framework that resolves this paradox. We present a mechanism for Finite Recursive Growth—an "Intelligence Ratchet"—that allows an AI system to exhibit transient bursts of superintelligent creativity while remaining rigorously bounded by physical and information-theoretic safety constraints. We formally define the Predictability Horizon (T_pred) for chaotic cognition and the Semantic Grounding Invariant that couples learning rates to physical verification.
DarcStar commentary
The field usually treats capability and safety as a straight trade: the more freely a system can rebuild itself, the less you can promise about where it ends up. This paper is our argument that the trade isn't fundamental. The "ratchet" is a way to let a system take real, transient bursts of creativity and still hold it inside physical and information-theoretic bounds it can't argue its way out of.
We put it out as a working paper because it's the frame the rest of our GIDE work has to earn — the claims here are the ones the engineering has to keep making true. It's a stake in the ground, not a finished proof, and we'd rather argue it in the open.