Capacity Plasticity in Hierarchical Inference: A Long-Horizon Extension of Precision Conservation

作者：Takashi Kubo · 发表于：Zenodo (CERN European Organization for Nuclear Research) · 年份：2026 · DOI：10.5281/zenodo.18993108 · 被引用次数：4 · 研究领域：Ecosystem dynamics and resilience、Genetics, Aging, and Longevity in Model Organisms、Reinforcement Learning in Robotics

Adaptive hierarchical systems regulate prediction-error weighting through precision, yet prior work has shown that such regulation is fundamentally constrained by a finite global precision budget. The earlier trilogy established three core results: (i) finite capacity induces a conservation-like geometric structure that confines redistribution to a capacity manifold; (ii) instability of redistribution modes on this manifold generates structural phase transitions; and (iii) meta-precision provides higher-order regulation of responsiveness near criticality without altering the admissible geometry. Together, these results define a local theory of bounded hierarchical responsiveness. What they do not determine is whether the capacity ceiling itself must remain fixed across longer developmental, overload-related, or transformative time scales. This paper introduces a developmental extension in which precision capacity is finite at any moment yet plastically variable across slower horizons of growth, overload, recovery, and structural reorganization. We distinguish three structurally distinct notions of capacity: nominal capacity, the formal upper bound on allocable precision; effective capacity, the stably usable range of coordinated regulation under that bound; and meta-capacity, the higher-order plastic potential through which the capacity ceiling itself may expand, contract, or reorganize. On this basis, we formulate a minimal four-time-scale dynamical framework in which the ca...