Neural networks calibrate 2D and 3D Dainotti relations on the Platinum GRB sample via ANN-driven MCMC to produce a model-independent Hubble diagram with reduced scatter.
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Dynamical dark energy remains preferred across extended models while curvature, neutrino mass and inflation parameters show strong model dependence, with no resolution of the H0 tension.
Phase-space analysis of a quadratic dark energy model identifies stable attractors that approach the phantom divide asymptotically from both sides without crossing it.
Re-analysis with PR4 Planck likelihoods reduces lensing anomaly significance and curvature preference in Lambda CDM extensions while indicating a preference for evolving dark energy consistent with DESI.