Effective Field Theory of Information Reconstruction

We present the Effective Field Theory of Information Reconstruction (EFIR), a mathematically rigorous framework that unifies three foundational structures: variational field theory, Fisher–Rao information geometry, and open quantum system dynamics. The framework models observer-limited access to a latent information field I(x,t) through a gauge-invariant action functional, from which the field equation of motion is derived via the Euler–Lagrange formalism. A local rescaling symmetry is introduced to account for different observer frames, and the associated gauge field and covariant derivative are constructed explicitly. The observer's belief state is represented as a point on a statistical manifold equipped with the Fisher–Rao metric, evolving along geodesics driven by incoming observations. Uncertainty in the reconstruction process is quantified through a density operator whose dynamics follow the Lindblad master equation, derived from first-principles measurement back-action via Kraus operators. The three components — field, geometry, and quantum state — are coupled into a closed, well-posed dynamical system on the phase space M_EFIR = {I, θ, ρ}. A reconstruction tensor R_μν is derived from first principles and shown to satisfy a variance bound expressible in terms of the Fisher–Ricci scalar, establishing a direct link between information curvature and reconstruction noise. In the Gaussian limit, the framework reduces exactly to the Kalman–Bucy filter, providing a non-trivial consistency check against classical optimal filtering theory. For a network of N observers, the synchronisation cost is proven to scale as O(N²), and decoherence is shown to grow monotonically with observer density. The paper includes three independently falsifiable scaling laws, a complete simulation protocol validated on a two-qubit toy model, and a discussion of the connection to emergent gravity via the Hessian of the von Neumann entropy.