Self-Organizing Survival Manifolds: A Theory for Unsupervised Discovery of Prognostic Structures in Biological Systems

TL;DR

This paper introduces a geometric theory called Self-Organizing Survival Manifolds (SOSM) that models survival as an emergent property of biological state space flow, independent of labeled data.

Contribution

It develops a novel, physics-grounded framework for understanding survival through geodesic flows on biological manifolds, unifying biophysical principles with geometric modeling.

Findings

Survival trajectories emerge from low-curvature geodesic flows.

The framework connects survival to thermodynamics, entropy, and optimal transport.

The theory demonstrates convergence of survival-aligned trajectories under plausible conditions.

Abstract

Survival is traditionally modeled as a supervised learning task, reliant on curated outcome labels and fixed covariates. This work rejects that premise. It proposes that survival is not an externally annotated target but a geometric consequence: an emergent property of the curvature and flow inherent in biological state space. We develop a theory of Self-Organizing Survival Manifolds (SOSM), in which survival-relevant dynamics arise from low-curvature geodesic flows on latent manifolds shaped by internal biological constraints. A survival energy functional based on geodesic curvature minimization is introduced and shown to induce structures where prognosis aligns with geometric flow stability. We derive discrete and continuous formulations of the objective and prove theoretical results demonstrating the emergence and convergence of survival-aligned trajectories under biologically plausible conditions. The framework draws connections to thermodynamic efficiency, entropy flow, Ricci curvature, and optimal transport, grounding survival modeling in physical law. Health, disease, aging, and death are reframed as geometric phase transitions in the manifold's structure. This theory offers a universal, label-free foundation for modeling survival as a property of form, not annotation-bridging machine learning, biophysics, and the geometry of life itself.