Evolving Afferent Architectures: Biologically-inspired Models for Damage-Avoidance Learning

• URL: http://arxiv.org/abs/2602.04807v1
• Date: Wed, 04 Feb 2026 17:53:28 GMT
• Title: Evolving Afferent Architectures: Biologically-inspired Models for Damage-Avoidance Learning
• Abstract summary: We introduce a framework that produces Computational Afferent Traces (CATs) as adaptive, internal risk signals for damage-avoidance learning.<n>We find that CAT-based evolved architectures achieve significantly higher efficiency and better age-robustness than hand-designed baselines.
• Score: 7.564968351077574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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