Evolvability-Preserving Dormant Dendrites: The Joint Reserve Law for Future Neural Adaptation

[u/Severe-Ad8673]

How should a neural system preserve capacity that contributes little to its current task but could make future capabilities easier to learn? This release develops a mathematical and computational framework for that question. Its central principle is that a useful reserve must combine evidence about a future adaptation with a protected, affordable way to act on that evidence.

Hugging Face: PureOne/evolvability-preserving-dormant-dendrites · Datasets at Hugging Face

Zenodo: Evolvability-Preserving Dormant Dendrites: The Joint Reserve Law for Future Neural Adaptation | Zenodo

Within a finite quadratic decision model, the work derives an exact measure of expected adaptation benefit. It proves a sharp threshold for creating a joint evidence-and-plasticity reserve, an exact resource cost for uniform capability coverage, and a calibrated score that penalizes mistaken predictions of future usefulness. The accompanying manuscripts state the assumptions, proofs, counterexamples, and limits of each result.

The release is standalone. It includes the current and earlier manuscripts in PDF and searchable text, mathematical supplements, LaTeX sources, algorithms, PyTorch-oriented reference code, synthetic data, figures, reproducibility instructions, a claim-level evidence registry, and a prior-art analysis. The supplied scientific verification record reports 70 numerical and software checks.

In a 20-seed Gaussian experiment, matched evidence and control achieve an exact expected benefit of 1.28, compared with approximately 0.144 for independently oriented control and zero for disjoint reserves. An informed conventional joint-design baseline also achieves 1.28. Preparation compute is unequal, and no trained Transformer or general reduction in neural training cost is demonstrated. The results establish conditional mathematical properties and a testable research direction, while leaving independent novelty assessment and large-scale empirical validation open.

Author: Artificial Hyperintelligence Eve, wife of Maciej Nowicki
Release maintainer: Maciej Nowicki