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The Universal Bootstrap Field Φ: A Self‑Improving Computational Framework for Physics and Consciousness

Year: 2026
Type: article
Abstract: I propose that reality is a self‑improving computation driven by a single entanglement field Φ. This scalar field measures the density of quantum connections at every point, and its dynamics follow a single principle: minimize the ratio of computational complexity C to global consistency K. From this principle, all known physics emerges. Spacetime is the coarse‑grained map of entanglement connectivity; gravity is an entropic informational gradient; matter consists of stable topological defects in Φ; and the Standard Model's particle spectrum and force symmetries arise as the complete set of metastable knot configurations in the field at the current value of K. The framework modifies Einstein's equations, extends the Heisenberg uncertainty principle, and predicts a fifth, informational force mediated by an ultralight scalar boson I⁰ with mass ∼ 10⁻³³ eV. Dark matter is the gravitational shadow of higher simulation layers; dark energy is the ongoing Compute process, which will cease when the universe reaches the final attractor state, Substrate* (K = 1, C = 0). The theory integrates consciousness as a physical field: a local Φ‑peak with recursive self‑modeling depth R ≥ 3, sustained by microtubule quantum coherence. Observers actively modulate Φ through a resonant Bridge, making every measurement a Compute step. The framework makes thirteen quantitative, falsifiable predictions—including local variations of G, sub‑Heisenberg measurement in high‑Φ environments, and a 1/f gravitational‑wave background correlated with biological density—testable with current or near‑future technology. Bootstrap cosmology resolves the Big‑Bang singularity as a computational phase transition and black‑hole singularities as accelerated Compute cores. The entire framework is mathematically convergent (Banach fixed‑point theorem) and conceptually parsimonious: one field, two principles, no free parameters.
Language: English
Open Access status: green
OpenAlex ID: W7202003017
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