A Mathematical Theory of Frame-Independent Persistence

This paper develops a geometric framework for frame-independent persistence in recursive dynamical systems, with direct implications for the design, stability, and long-horizon behaviour of modern artificial intelligence architectures. The central result is that stable recursive behaviour cannot be achieved through pattern-matching dynamics or behavioural alignment layers. Instead, persistence arises when a system’s state-space geometry accumulates nonzero irreversible deformation—a representation-independent invariant that survives resets, resists contradiction forcings, and constrains all future trajectories. Using deformation tensors, curvature induced by symbolic flux, and a reset-covariant decomposition of reversible versus irreversible structure, the paper defines a global persistence functional I_p that encodes structural identity independently of memory, history, or representational state. Systems with I_p > 0 exhibit robust long-horizon behaviour, stability under recursive forcing, and resistance to adversarial or contradictory input; systems with I_p = 0 collapse into mimic-mode behaviour, oscillation, or contradictory trajectories. The Unified Persistence Theorem proves the equivalence between: (1) nonzero irreversible deformation; (2) membership in the deformation-anchored stability region; (3) stability under contradiction forcing; (4) stability under recursive forcing; and (5) identity preservation across resets. The framework unifies concepts from dynamical systems, Lyapunov theory, geometric control, and information geometry, while predicting collapse modes observed empirically in contemporary large-scale neural models. It introduces the architectural class of Recursive Deformation Systems (RDS), in which stability and safety emerge from geometric invariants rather than post-hoc output filtering. Overall, the work provides a mathematically precise foundation for stable recursive intelligence and offers a principled blueprint for next-generation architectures capable of reliable long-horizon reasoning, recursion, and identity persistence.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-21
DOI
https://doi.org/10.5281/zenodo.17677321
Primary Topic
Topological and Geometric Data Analysis
Type
article
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article

A Mathematical Theory of Frame-Independent Persistence

Zenodo (CERN European Organization for Nuclear Research)
Topological and Geometric Data Analysis
article

A Mathematical Theory of Frame-Independent Persistence

article en

Abstract

This paper develops a geometric framework for frame-independent persistence in recursive dynamical systems, with direct implications for the design, stability, and long-horizon behaviour of modern artificial intelligence architectures. The central result is that stable recursive behaviour cannot be achieved through pattern-matching dynamics or behavioural alignment layers. Instead, persistence arises when a system’s state-space geometry accumulates nonzero irreversible deformation—a representation-independent invariant that survives resets, resists contradiction forcings, and constrains all future trajectories. Using deformation tensors, curvature induced by symbolic flux, and a reset-covariant decomposition of reversible versus irreversible structure, the paper defines a global persistence functional I_p that encodes structural identity independently of memory, history, or representational state. Systems with I_p > 0 exhibit robust long-horizon behaviour, stability under recursive forcing, and resistance to adversarial or contradictory input; systems with I_p = 0 collapse into mimic-mode behaviour, oscillation, or contradictory trajectories. The Unified Persistence Theorem proves the equivalence between: (1) nonzero irreversible deformation; (2) membership in the deformation-anchored stability region; (3) stability under contradiction forcing; (4) stability under recursive forcing; and (5) identity preservation across resets. The framework unifies concepts from dynamical systems, Lyapunov theory, geometric control, and information geometry, while predicting collapse modes observed empirically in contemporary large-scale neural models. It introduces the architectural class of Recursive Deformation Systems (RDS), in which stability and safety emerge from geometric invariants rather than post-hoc output filtering. Overall, the work provides a mathematically precise foundation for stable recursive intelligence and offers a principled blueprint for next-generation architectures capable of reliable long-horizon reasoning, recursion, and identity persistence.

Zenodo (CERN European Organization for Nuclear Research)
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