Resonant Spatial Computing: Dynamic Synchronization and the Gravitational Anchor

This monograph formalizes the paradigm of Resonant Spatial Computing (RSC). We propose an alternative to static geometric calibration: a protocol of Dynamic Inertial Synchronization (IPS) that couples the interface to the geodetic field via a Geocentric Pendulum. Classical mechanics is used as a primary analog data compressor—a stage we define as Layer Zero. The framework draws on Arboreal Priors and the Precision Grip as evolutionary substrates, providing a physical anchor for Active Inference and the Free Energy Principle. RSC addresses the Sparsity Paradox by extracting intent at the Kinetic Nadir, achieving a 150:1 data reduction. We provide numerical simulations demonstrating noise suppression and sparse reconstruction, illustrating how gravitational priors reduce proprioceptive drift.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-23
DOI
https://doi.org/10.5281/zenodo.21479112
Primary Topic
Modular Robots and Swarm Intelligence
Type
preprint
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preprint

Resonant Spatial Computing: Dynamic Synchronization and the Gravitational Anchor

André Luiz Trindade
Zenodo (CERN European Organization for Nuclear Research)
Modular Robots and Swarm Intelligence
preprint

Resonant Spatial Computing: Dynamic Synchronization and the Gravitational Anchor

André Luiz Trindade
preprint en

Abstract

This monograph formalizes the paradigm of Resonant Spatial Computing (RSC). We propose an alternative to static geometric calibration: a protocol of Dynamic Inertial Synchronization (IPS) that couples the interface to the geodetic field via a Geocentric Pendulum. Classical mechanics is used as a primary analog data compressor—a stage we define as Layer Zero. The framework draws on Arboreal Priors and the Precision Grip as evolutionary substrates, providing a physical anchor for Active Inference and the Free Energy Principle. RSC addresses the Sparsity Paradox by extracting intent at the Kinetic Nadir, achieving a 150:1 data reduction. We provide numerical simulations demonstrating noise suppression and sparse reconstruction, illustrating how gravitational priors reduce proprioceptive drift.

Zenodo (CERN European Organization for Nuclear Research)
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Modular Robots and Swarm Intelligence
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