Abstract This work presents Version 2 of the Universe Force & Mirror Universe Unified Theory, reformulated as a phenomenological scalar–tensor braneworld cosmological framework. The model departs from the original propagator sign-flip hypothesis and instead describes the separation between two cosmological branes through a dynamical radion field. The radion evolution generates an effective dark energy sector and provides a mechanism for transient Early Dark Energy (EDE) through a localized resonance associated with extra-dimensional Kaluza–Klein dynamics. Within the background cosmology approximation, the framework reproduces a late-time vacuum-energy-dominated expansion history while allowing a temporary enhancement of the expansion rate near the pre-recombination epoch. This behavior motivates the model as a potential Early Dark Energy candidate capable of reducing the cosmological sound horizon and potentially alleviating the Hubble tension. The model further incorporates baryon-loaded sound-speed corrections and a stabilized matter sector designed to preserve standard matter-radiation equality and large-scale structure evolution at the background level. Scientific Status and Limitations This work should be regarded as a theoretical background-cosmology framework rather than a completed cosmological model. The analysis presented here is limited to homogeneous background evolution and has not yet been tested against full Cosmic Microwave Background (CMB) anisotropy data, Baryon Acoustic Oscillation (BAO) measurements, weak-lensing observations, supernova datasets, or large-scale structure constraints. A rigorous derivation of the effective four-dimensional action, linear perturbation equations, and Einstein–Boltzmann implementations remain subjects of future investigation. AI Collaboration Statement The original conceptual ideas, including the mirror-universe interpretation, braneworld structure, and black-hole/white-hole bridge hypothesis, were conceived by the author. Due to the advanced mathematical and cosmological techniques required for model development, extensive assistance from Large Language Models (LLMs) was utilized throughout the research process. These AI systems contributed to mathematical formalization, equation development, numerical simulation design, consistency checks, and scientific presentation. The final framework should therefore be understood as a collaborative human–AI research effort in which the physical concepts originated from the author while significant mathematical implementation and refinement were achieved with AI assistance.
Maneth Sethnal Wijimanna (Tue,) studied this question.