Adaptive Learning Guided by Bias-Noise-Alignment Diagnostics

• URL: http://arxiv.org/abs/2512.24445v2
• Date: Fri, 02 Jan 2026 17:32:09 GMT
• Title: Adaptive Learning Guided by Bias-Noise-Alignment Diagnostics
• Authors: Akash Samanta, Sheldon Williamson,
• Abstract summary: This paper proposes a diagnostic-driven learning framework that explicitly models error adaptive evolution.<n>These diagnostics are computed online from lightweight statistics of loss or temporal-difference (TD) error trajectories.
• Score: 0.7519872646378835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning systems deployed in nonstationary and safety-critical environments often suffer from instability, slow convergence, or brittle adaptation when learning dynamics evolve over time. While modern optimization, reinforcement learning, and meta-learning methods adapt to gradient statistics, they largely ignore the temporal structure of the error signal itself. This paper proposes a diagnostic-driven adaptive learning framework that explicitly models error evolution through a principled decomposition into bias, capturing persistent drift; noise, capturing stochastic variability; and alignment, capturing repeated directional excitation leading to overshoot. These diagnostics are computed online from lightweight statistics of loss or temporal-difference (TD) error trajectories and are independent of model architecture or task domain. We show that the proposed bias-noise-alignment decomposition provides a unifying control backbone for supervised optimization, actor-critic reinforcement learning, and learned optimizers. Within this framework, we introduce three diagnostic-driven instantiations: the Human-inspired Supervised Adaptive Optimizer (HSAO), Hybrid Error-Diagnostic Reinforcement Learning (HED-RL) for actor-critic methods, and the Meta-Learned Learning Policy (MLLP). Under standard smoothness assumptions, we establish bounded effective updates and stability properties for all cases. Representative diagnostic illustrations in actor-critic learning highlight how the proposed signals modulate adaptation in response to TD error structure. Overall, this work elevates error evolution to a first-class object in adaptive learning and provides an interpretable, lightweight foundation for reliable learning in dynamic environments.

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