Integration with tree-based and deep base learners

Develop Fibonacci weighting schemes for tree-based ensemble learners, including random forests and gradient-boosted trees, as a meta-aggregation layer or structural prior on stage-wise additions, and investigate Fibonacci-inspired layer or block weighting in deep neural networks with residual connections to realize second-order recursive influences.

Background

Earlier sections restrict attention to smooth base learners and discuss why tree-based models complicate spectral analysis and orthogonalization. The authors suggest moving beyond this limitation by incorporating Fibonacci weighting into tree ensembles and deep architectures.

In the Future Work section, they propose exploring Fibonacci weighting as a meta-aggregation over tree-based ensembles and as a structural prior in boosted trees, as well as considering Fibonacci-inspired layer or block weighting in deep neural networks.

References

Several directions remain open and are, in our view, both challenging and promising.

— On Fibonacci Ensembles: An Alternative Approach to Ensemble Learning Inspired by the Timeless Architecture of the Golden Ratio  (2512.22284 - Fokoué, 25 Dec 2025) in Section “Future Work: From One-Dimensional Harmony to High Dimensional Practice”