Drive-Field Information Theory. A Holographic Entropic Model for Emergent Gravity

The standard cosmological model, General Relativity (GR) coupled with Λ-Cold Dark Matter (ΛCDM), has achieved remarkable empirical success but faces unresolved challenges, including singularities, the black hole information paradox, observational tensions in the Hubble constant (!!) and structure growth parameter (""), and reliance on unseen dark components comprising over 95% of the cosmic energy budget. This paper introduces Drive-Field Information Theory (DFIT), an exploratory framework in which gravity emerges from gradients in a dimensionless holographic entropy field ##$%$, representing coarse-grained quantum entanglement structures. The dynamics follow from a covariant action whose variation yields modified Einstein equations with an information- stress tensor $ in place of the conventional energy-momentum tensor. &' In the limit of vanishing entropy gradients (%##$%$ → 0), DFIT reduces to GR with an effective cosmological constant, ensuring consistency with established tests. In gradient-rich regimes, however, it introduces scale- dependent corrections that provide a unified informational perspective on phenomena often attributed to dark energy, dark matter, and black hole thermodynamics. Methodological explorations using tensor networks and MERA illustrate how DFIT could, in principle, produce testable signatures—including redshift-dependent deviations in luminosity distances, modifications to structure growth, or subtle effects on gravitational waves and quasinormal modes—potentially observable with surveys such as DESI, Euclid, Gaia, the Event Horizon Telescope, and LISA. DFIT is presented not as a replacement for GR+ΛCDM but as a falsifiable, holographically motivated extension, framing gravity as a macroscopic response to underlying informational fluxes. Future progress will require microscopic derivations of ##$%$, scalable tensor-network simulations, and quantitative confrontation with astrophysical and cosmological datasets.