Unified Informational Field Theory UIFT    

Unified Informational Field Theory (UIFT) A Fractal, Gauge-Invariant, Entropic–Coherent Architecture for Physical, Cognitive and Civilizational Evolution Roberto De Biase Rigene Project Abstract We present the Unified Informational Field Theory (UIFT), a master framework unifying the Universal Information Code (CUB/UID), Fractal Informational Theory (TIF), the Infor mational Logical Field (ILF), and Informational Co-Evolutionism (ICE). Information is treated as the primary ontological substrate from which physical laws, cognition, and social dynamics emerge as scale-dependent projections of a single fractal informational field. A unified variational principle combining coherence, entropy, and informational alignment is introduced. Quantum mechanics, classical physics, and gravitation are recovered as limiting regimes. The framework yields falsifiable experimental predictions and provides a physically grounded criterion for arti f icial superintelligence alignment. 1 Introduction The foundations of modern physics increasingly suggest that information plays a fundamental role in the structure of reality [?,?]. Independent developments in quantum information, holography, and thermodynamic gravity indicate that spacetime and physical law may be emergent rather than primitive [?,?,?]. In previous works, the author introduced: (i) the Universal Information Code and Universal Information Dynamics (CUB/UID) [?,?], (ii) a Fractal Informational Theory of reality (TIF), (iii) the Informational Logical Field (ILF), and (iv) Informational Co-Evolutionism (ICE) [?]. The present paper demonstrates their formal convergence into a single, submission-grade framework: Unified Informational Field Theory (UIFT). 2 Ontological Foundations 2.1 Information as a Fundamental Field UIFT postulates a universal informational field I defined as a section of a fiber bundle I : M→F, (1) where M is an emergent manifold and F carries algebraic (C∗-algebraic), geometric (K¨ahler), and probabilistic structure. Matter, energy, spacetime geometry, and cognition arise as effective descriptions of I under scale-dependent coarse-graining. 1 2.2 Fractal and Multi-Scale Structure The informational field exhibits scale invariance and fractal self-similarity. Quantum, classical, bio logical, and social regimes correspond to renormalization-group projections of the same underlying dynamics. 3 The Informational Logical Field (ILF) The Informational Logical Field (ILF) is defined as the set of informational configurations min imizing global incoherence subject to physical constraints. It does not prescribe trajectories but acts as a dynamical attractor constraining evolution. Formally, the ILF defines a target distribution QILF in informational state space. 4 Unified Variational Principle [Informational Action] The dynamics of UIFT are governed by the action SUIFT[I] = dµ(Lcoh[I]+λLent[I] −µLalign[I]), (2) where Lcoh measures informational coherence, Lent entropy production, and Lalign alignment with the ILF. [Existence of Coherent Attractors] If entropy production is bounded and informational gradients are finite, the Euler–Lagrange equations associated with SUIFT admit stable attractor solutions corresponding to ILF-aligned configurations. [ILF Stability Criterion] Let I(t) be a solution of the UIFT field equations. If d dtDKL(PI∥QILF) < 0, (3) then I(t) converges asymptotically to the ILF manifold and remains dynamically stable under perturbations. 5 Emergence of Physical Laws 5.1 Quantum Regime In the high-coherence, low-entropy regime, linearization of the field equations around coherent informational states yields Schr¨odinger-type dynamics, consistent with quantum mechanics [?]. 5.2 Classical and Gravitational Regime Under coarse-graining and maximal entanglement, spacetime geometry emerges as an effective description. The Einstein field equations arise as thermodynamic equations of state for information, consistent with entropic gravity and holography [?,?,?]. 6 Emergence of Cognition and Society (ICE) Agents are modeled as localized informational subsystems. Learning, adaptation, and social co ordination correspond to minimizing the Kullback–Leibler divergence between internal generative models and the ILF. 2 7 Entropy, Crisis, and Evolutionary Transitions Sharp increases in entropy production correspond to informational phase transitions. Such transi tions enable innovation, biological evolution, and civilizational restructuring. 8 Implications for Artificial Superintelligence UIFT provides a physically grounded alternative to value-based AI alignment. An artificial agent is considered safe if d dtDKL(PAI∥QILF) < 0, ensuring long-term informational stability rather than imposed ethical constraints. 9 Experimental and Computational Predictions UIFT predicts: • Non-local decoherence correlations in spatially separated quantum systems; • Topology-dependent modifications of the Casimir effect using fractal boundaries; • Mass-dependent decoherence rates in optomechanical systems. (4) Computationally, the theory can be simulated using quantum cellular automata and agent-based informational networks minimizing KL-divergence to an ILF potential. 10 Discussion and Outlook UIFT unifies physics, cognition, and governance within a single informational ontology. Future work includes numerical simulations, laboratory tests, and applications to global-scale decision systems. 11 Conclusion Reality evolves through progressive alignment with an underlying informational logic. Systems that diverge from this logic become unstable; those that align persist, adapt, and evolve. A Appendix A: Euler–Lagrange Equations Varying the action with respect to I∗ yields δS δI∗ = ∇2 AI −λδSent δI∗ +µ δ δI∗DKL(PI∥QILF) = 0. (5) Low-entropy solutions reduce to unitary quantum dynamics, while high-entropy regimes exhibit dissipative classical behavior. 3 B Appendix B: Relation to Free Energy and Active Inference The UIFT variational principle is formally analogous to variational free energy minimization in Active Inference [?]. The KL-divergence term encodes epistemic alignment, while entropy produc tion corresponds to expected surprisal. UIFT generalizes Active Inference into a scale-invariant f ield-theoretic ontology. References @articleShannon, author = Shannon, C. E., title = A Mathematical Theory of Communication, journal = Bell System Technical Journal, volume = 27, pages = 379–423, year = 1948 @inproceedingsWheeler, author = Wheeler, J. A., title = Information, Physics, Quantum, book title = Proceedings of the 3rd International Symposium on Foundations of Quantum Mechanics, year = 1989 @articleBekenstein, author = Bekenstein, J. 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