Dynamic nested hierarchies: self-evolving machine learning architectures for lifelong learning

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computational Neuroscience

Background:

• Current machine learning models struggle with non-stationary environments due to rigid architectures, limiting continual adaptation and lifelong learning.
• The nested learning (NL) paradigm offers a framework for multi-level optimization but lacks dynamic structural adaptation.
• Existing continual learning methods like Elastic Weight Consolidation (EWC) and Synaptic Intelligence (SI) have limitations in dynamic environments.

Purpose of the Study:

• To introduce Dynamic Nested Hierarchies (DNH), an extension of nested learning that enables autonomous structural adaptation in machine learning models.
• To develop biologically-grounded mechanisms for dynamic hierarchy adjustment, inspired by neuroplasticity.
• To provide theoretical guarantees and empirical validation for DNH's effectiveness in non-stationary environments and continual learning tasks.

Main Methods:

• Proposed DNH with three core mechanisms: level addition (meta-loss thresholds), level pruning (gradient contribution), and frequency modulation (surprise signals).
• Developed explicit mappings between DNH mechanisms and neuroplasticity processes (neurogenesis, synaptic elimination, neural oscillation adaptation).
• Conducted rigorous mathematical analysis to derive convergence bounds, expressivity improvements, and regret bounds.
• Performed empirical evaluations on language modeling, continual learning benchmarks (Split ImageNet, CLEAR-100, CORe50), and long-context reasoning tasks.
• Included ablation studies on the Self-Modifying Memory (SMM) module and Evolutionary Adam (EAdam) optimizer.

Main Results:

• Proved theoretical convergence bounds of O(1/T + δ^2) in non-stationary environments and sublinear regret O(sqrt(T)).
• Demonstrated improved expressivity bounded by ϵ ≤ O(1/L_t) + γδ.
• Empirical results validated DNH's advantages over static architectures and compared favorably with EWC, SI, DER++, and MEMO on various benchmarks.
• Ablation studies confirmed the contribution of individual DNH components, SMM, and EAdam.
• Computational cost analysis and parameter visualizations showed bounded growth through self-regulating pruning.

Conclusions:

• Dynamic Nested Hierarchies (DNH) offer a principled and effective approach for enabling lifelong learning and adaptation in machine learning models.
• The biologically-grounded mechanisms provide a robust framework for autonomous structural adaptation, outperforming static architectures in non-stationary environments.
• DNH represents a significant advancement in continual learning, with broad applicability in complex AI systems.