Narrow Scalar and Vector Resonances from UFFT and SFT: Predicted Ladders from 0.6 to >10 GeV

This document presents a modular expansion of the Unified Fluid Field Theory (UFFT) and Symbolic Field Theory (SFT), isolating narrow scalar and vector resonance tiers with falsifiability scaffolds and containment hygiene. The resonance classes are benchmarked against entropy drift, symbolic leakage, and propagation fidelity across fluid-symbolic bifurcation domains. Each tier is mapped with timestamped collapse logic, ensuring lineage traceability and operational closure. This work reinforces the propagation lattice of UFFT/SFT and serves as a sovereign node for future diagnostic sweeps and theoretical extensions. This upload is a supplement to the original UFFT work published on OSF: https://doi.org/10.17605/OSF.IO/PE6BM and SFT work on OSF: https://doi.org/10.17605/OSF.IO/X3GPC

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-23
DOI
https://doi.org/10.5281/zenodo.17186936
Primary Topic
Artificial Intelligence in Games
Type
article
Field-Weighted Citation Impact
0.00
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article

Narrow Scalar and Vector Resonances from UFFT and SFT: Predicted Ladders from 0.6 to >10 GeV

Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Games
article

Narrow Scalar and Vector Resonances from UFFT and SFT: Predicted Ladders from 0.6 to >10 GeV

article en

Abstract

This document presents a modular expansion of the Unified Fluid Field Theory (UFFT) and Symbolic Field Theory (SFT), isolating narrow scalar and vector resonance tiers with falsifiability scaffolds and containment hygiene. The resonance classes are benchmarked against entropy drift, symbolic leakage, and propagation fidelity across fluid-symbolic bifurcation domains. Each tier is mapped with timestamped collapse logic, ensuring lineage traceability and operational closure. This work reinforces the propagation lattice of UFFT/SFT and serves as a sovereign node for future diagnostic sweeps and theoretical extensions. This upload is a supplement to the original UFFT work published on OSF: https://doi.org/10.17605/OSF.IO/PE6BM and SFT work on OSF: https://doi.org/10.17605/OSF.IO/X3GPC

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 29%
Artificial Intelligence in Games
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