We introduce Afferent Learning, a framework that produces Computational Afferent Traces (CATs) as adaptive, internal risk signals for damage-avoidance learning. Inspired by biological systems, the framework uses a two-level architecture: evolutionary optimization (outer loop) discovers afferent sensing architectures that enable effective policy learning, while reinforcement learning (inner loop) trains damage-avoidance policies using these signals. This formalizes afferent sensing as providing an inductive bias for efficient learning: architectures are selected based on their ability to enable effective learning (rather than directly minimizing damage). We provide theoretical convergence guarantees under smoothness and bounded-noise assumptions. We illustrate the general approach in the challenging context of biomechanical digital twins operating over long time horizons (multiple decades of the life-course). Here, we find that CAT-based evolved architectures achieve significantly higher efficiency and better age-robustness than hand-designed baselines, enabling policies that exhibit age-dependent behavioral adaptation (23% reduction in high-risk actions). Ablation studies validate CAT signals, evolution, and predictive discrepancy as essential. We release code and data for reproducibility.

翻译：本文提出传入学习框架，该框架通过生成计算性传入痕迹作为自适应内部风险信号，用于损伤规避学习。受生物系统启发，该框架采用双层架构：进化优化（外循环）发现能够支持有效策略学习的传入感知架构，而强化学习（内循环）则利用这些信号训练损伤规避策略。该框架将传入感知形式化为高效学习的归纳偏置提供机制：架构选择依据是其支持有效学习的能力（而非直接最小化损伤）。我们在平滑性和有界噪声假设下提供了理论收敛保证。我们在生物力学数字孪生长时间运行（跨越生命历程数十年）的挑战性场景中展示了该通用方法。实验表明，基于CAT的演化架构相较于人工设计的基线实现了显著更高的效率与更好的年龄鲁棒性，使策略能够呈现年龄依赖的行为适应（高风险动作减少23%）。消融研究验证了CAT信号、进化机制与预测差异度量的必要性。我们已开源代码与数据以确保可复现性。