Title: Transitioning from Performance to Viability: A Bootstrap Framework for Latent-Space Representation Learning in Adaptive Biological Systems

Abstract:

While observable performance is the standard metric for characterizing biological systems, it presents significant limitations in the context of adaptive systems. In these environments, identical performance outcomes can stem from disparate organizational structures, and configurations that seem equivalent at a specific moment may diverge over time. To address this, we propose a methodological framework that transcends performance-based interpretations without requiring a pre-defined, complete mechanistic model.

This paper introduces a bootstrap framework designed for latent-space representation learning within adaptive biological systems. Here, the term "bootstrap" is employed both methodologically and epistemologically, referring to the introduction of new analytical layers when previous representations fail to adequately explain observed adaptive dynamics. The framework is structured around five distinct levels: observable performance, dynamic organization, latent organization, longitudinal viability, and internal predictive approximation.

To demonstrate the utility of this approach, we apply the framework to three previously published studies on gait occlusion. These studies serve strictly as a methodological case sequence rather than presenting new experimental data. The article details the formal progression from performance analysis to the identification of latent organization, the shift from static latent organization to longitudinal viability, and the emergence of internal predictive approximation from observed viability.

It is important to clarify that this work does not introduce a novel learning algorithm, a clinical protocol, or a new dataset. Instead, it offers a bootstrap framework for latent-space representation learning that elucidates how increasingly informative representations can arise from the insufficiencies inherent in observational adaptive biological data.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC