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arXiv Machine Learning July 14, 2026 By Jacques Raynal, Pierre Slangen, Elsa Raynal, Jacques Margerit

From Performance to Representational Adequacy: A Representational Bootstrap Framework for Adaptive Biological Systems

Read the original on arXiv Machine Learning →

arXiv:2606. 01374v3 Announce Type: replace Abstract: Observable performance is commonly used to characterize biological systems, yet aggregated outputs may remain insufficient for uniquely resolving observational conditions, and richer multivariate representations may retain substantial ambiguity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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arXiv Machine Learning

Jun 2

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

arXiv:2606. 01374v1 Announce Type: new Abstract: Observable performance is commonly used to characterize biological systems.

By Jacques Raynal, Pierre Slangen, Elsa Raynal, Jacques Margerit

arXiv Machine Learning

Jul 14

Observable Performance Does Not Fully Reflect Adaptive System Organization: A Multi-Level Analysis of Gait Dynamics Under Occlusal Constraint

arXiv:2605. 00778v2 Announce Type: replace Abstract: In biomechanical systems, observable performance is often used as a proxy for underlying organization, although similar outputs may arise from different adaptive configurations.

By Jacques Raynal, Pierre Slangen, Elsa Raynal, Jacques Margerit

arXiv Machine Learning

Jul 14

From Observed Viability to Internal Predictive Approximation: A Single-Subject Latent-Space Analysis of Gait Dynamics Under Occlusal Constraint

arXiv:2605. 15862v2 Announce Type: replace Abstract: Understanding adaptive biomechanical systems requires distinguishing observable performance, static multivariate representation, longitudinal displacement, and internal approximation of observed change.

By Jacques Raynal, Pierre Slangen, Elsa Raynal, Jacques Margerit

arXiv Machine Learning

Aug 21

Bootstrap Theory of Representational Emergence (TBER): Explanatory Insufficiency, Transition Regimes, and the Emergence of New Representational Levels

arXiv:2606. 07303v4 Announce Type: replace Abstract: Representation learning is central to modern machine learning, yet most research focuses on optimizing representations after a framework has been selected.

By Jacques Raynal, Pierre Slangen, Elsa Raynal, Jacques Margerit

arXiv Machine Learning

Jun 8

Bootstrap Theory of Representational Emergence: Explanatory Insufficiency as a Driver of Representation Learning and World Models

arXiv:2606. 07303v1 Announce Type: new Abstract: Representation learning is central to modern machine learning, enabling transitions from handcrafted features to learned embeddings, latent spaces, foundation models, world models, and digital twins.

By Jacques Raynal, Pierre Slangen, Elsa Raynal, Jacques Margerit

arXiv AI

Sep 23

A Compact Stance-Indexed Anterior-Posterior COP Representation for Parkinson's Disease Classification from Plantar VGRF

arXiv:2609.22956v1 Announce Type: new Abstract: Parkinson's disease alters gait and bilateral coordination, but machine-learning performance also depends on how continuous gait signals are represente...

By Md. Sifat, Sania Akter, Akif Islam, Md. Ekramul Hamid