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 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. Building on this framework, we derive diagnostic-driven instantiations including a stabilized supervised optimizer, a diagnostic-regulated actor-critic scheme, and a diagnostic-conditioned learned optimizer. 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 temporal-difference 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.

翻译：部署于非平稳与安全关键环境中的学习系统，常因学习动态随时间演变而遭受不稳定、收敛缓慢或脆弱的适应性问题。尽管现代优化、强化学习和元学习方法能够适应梯度统计量，但它们大多忽略了误差信号本身的时间结构。本文提出一种诊断驱动的自适应学习框架，通过将误差演化原则性地分解为偏置（捕捉持续漂移）、噪声（捕捉随机波动）和对齐（捕捉导致超调的重发性方向激励）来显式建模误差演化过程。这些诊断指标可从损失或时序差分误差轨迹的轻量级统计量在线计算，且独立于模型架构或任务领域。我们证明，所提出的偏置-噪声-对齐分解为监督优化、演员-评论家强化学习及学习型优化器提供了统一的控制框架。基于此框架，我们推导出诊断驱动的具体实现方案，包括稳定化监督优化器、诊断调节型演员-评论家方案以及诊断条件化学习型优化器。在标准平滑性假设下，我们为所有案例建立了有效更新边界与稳定性证明。演员-评论家学习中的典型诊断图示突显了所提信号如何根据时序差分误差结构调节适应过程。总体而言，本研究将误差演化提升为自适应学习中的核心对象，并为动态环境中的可靠学习提供了可解释、轻量化的理论基础。