A Liminal Topological Bridge for Lossless Continuous-to-Discrete Translation Reducing the Hidden Cost of Mode Switching in Autonomous and Hybrid Systems

Modern computational systems incur a recurring information loss , often called the “conversion tax” , whenever they move between rich, continuous, probabilistic representations and precise, deterministic outputs. This friction is particularly costly in autonomous and high-reliability systems that must process high-dimensional sensor data while generating reliable control actions.This note presents a high-level overview of a continuous-time, asynchronous architecture that introduces a non-orientable topological boundary layer as a native liminal third state. Rather than relying on conventional quantising converters, the approach routes information across a structured mathematical surface, enabling bidirectional translation between probabilistic fields and deterministic coordinates while preserving information through topological rerouting.The proposed architecture is designed to be fully asynchronous and clock-free, bidirectional by construction, and information-preserving by design. It is particularly relevant to domains that require fluid transitions between perception and control, including autonomous vehicles, spacecraft, robotics, and hybrid AI systems.A complete mathematical treatment, including the dynamical formulation, proofs of well-posedness and bidirectional symmetry, and numerical verification , has been developed. The full technical paper is available Continuous-Time Asynchronous Architecturev4b 10.5281/zenodo.20437222

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-29
DOI
https://doi.org/10.5281/zenodo.20450680
Primary Topic
Model Reduction and Neural Networks
Type
article
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article

A Liminal Topological Bridge for Lossless Continuous-to-Discrete Translation Reducing the Hidden Cost of Mode Switching in Autonomous and Hybrid Systems

James Stinton-duGard
Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
article

A Liminal Topological Bridge for Lossless Continuous-to-Discrete Translation Reducing the Hidden Cost of Mode Switching in Autonomous and Hybrid Systems

James Stinton-duGard
article en

Abstract

Modern computational systems incur a recurring information loss , often called the “conversion tax” , whenever they move between rich, continuous, probabilistic representations and precise, deterministic outputs. This friction is particularly costly in autonomous and high-reliability systems that must process high-dimensional sensor data while generating reliable control actions.This note presents a high-level overview of a continuous-time, asynchronous architecture that introduces a non-orientable topological boundary layer as a native liminal third state. Rather than relying on conventional quantising converters, the approach routes information across a structured mathematical surface, enabling bidirectional translation between probabilistic fields and deterministic coordinates while preserving information through topological rerouting.The proposed architecture is designed to be fully asynchronous and clock-free, bidirectional by construction, and information-preserving by design. It is particularly relevant to domains that require fluid transitions between perception and control, including autonomous vehicles, spacecraft, robotics, and hybrid AI systems.A complete mathematical treatment, including the dynamical formulation, proofs of well-posedness and bidirectional symmetry, and numerical verification , has been developed. The full technical paper is available Continuous-Time Asynchronous Architecturev4b 10.5281/zenodo.20437222

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 5%
Model Reduction and Neural Networks
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