Infogravity 4.6 : Operational Detection of Informational Regime

Infogravity 4.6 operationalizes the notion of informational regimes by introducing a likelihood-based diagnostic pipeline directly applicable to data. Resolution is treated as an explicit coarse–graining parameter s implemented by a semigroup of channels, and regime structure is defined by stability under resolution flow rather than by new forces or microscopic dynamics. From posteriors across resolutions, we construct three computable diagnostics: the Fisher information matrix F_s (and invariants such as \log\det F_s), an information–loss rate \Gamma_s defined via KL divergences and constrained by the data–processing inequality, and an effective dimensionality d_{\rm eff}(s) tracking the collapse of sufficient statistics. We show that regime transitions admit a universal, falsifiable signature: a correlated concurrence of a peak in \Gamma_s, Fisher-spectrum reorganization, and dimensional collapse. A minimal Gaussian-mixture toy model provides an existence proof, and we outline concrete resolution families (binning, smoothing, bandwidth truncation) enabling direct application to observational domains such as large-scale structure, precision clocks, and gravitational-wave inference.