Theoretical Limits of Recursive Self-Improvement: Implications for Next-Gen GPU Design
Recursive self-improvement GPU roadmaps often assume autonomous training loops require ever-more accelerators. Hector Zenil’s analysis (arXiv:2601.05280, January 2026 preprint, King’s College London) models recursive self-training as a discrete-time dynamical system: when the proportion of exogenous (externally grounded) signal αt→0, closed-loop density matching suffers entropy decay and variance amplification—mathematical limits, not engineering inconveniences. Thesis: Pure autonomous … Read more