Using Fibonacci sequence-based context lengths consistently outperforms linear or exponential spacing on heterogeneous datasets.
Traditional Linear Spacing:
k = {1, 2, 3, 4, 5, 6, 7, 8, ...}Uniform coverage, but inefficient resource allocation
Fibonacci Spacing:
k = {1, 2, 3, 5, 8, 13, 21, 34, ...}Better adaptation to natural pattern scales
The golden ratio scaling of Fibonacci numbers naturally aligns with hierarchical pattern structures in mixed-content data (XML tags, natural language phrases, repetitive markup). This provides optimal coverage of both short-range and long-range dependencies without redundant overlap.
Periodically injecting controlled stochastic perturbations (quantum noise) into the probability distribution improves prediction accuracy.
P'(s) = (1 - ε) · P(s) + ε · Q(s)
where Q(s) ~ quantum noise distribution, ε ≈ 0.001-0.01Adding deliberate noise to a predictor seems wrong, but it acts as regularization—preventing the model from becoming too confident on rare patterns that may not recur. This is analogous to dropout in neural networks or simulated annealing in optimization. The small entropy cost from noise is offset by avoiding catastrophic mispredictions on novel sequences.