The ΛCDM model generally describes the cosmic microwave background (CMB) with precision, however, a cluster of anomalies persists at the largest angular scales. These include a low-ℓ power deficit, quadrupole / octopole alignments and planarity, odd/even parity asymmetry, and a hemispherical power asymmetry. An independent tension is observed in extragalactic source counts, where the observed dipole exceeds the kinematic expectation by a factor ≳ 2, approaching 5σ. These anomalies appear distinct, yet several share a direct relationship to the observer's vantage point or motion. This raises the possibility that the act of measurement is not incidental. This study hypothesises that they can be unified within a single recording architecture centred on the observer's causal diamond, treated as a finite information aperture. The Casini–Huerta–Myers modular Hamiltonian supplies the geometric reference state, absorbing Lindblad dynamics implement irreversible registration, and a decoder taxonomy classifies the anomalies by multipole order and tensor rank. ΛCDM is retained while the anomalies are attributed to the geometry and kinematics of the recording process itself. The derived framework has zero fitted parameters. A derived angular filter and signed antipodal operator address the low-ℓ power deficit and parity asymmetry, shifting Planck S₁/₂ from the 0.3rd to the 2.8th percentile (2Δ ln ℒ = 5.6, p = 0.011, ΔAIC = −5.6 vs ΛCDM). The radial kernel predicts source-count amplification 𝒜 = 5.0 against a CatWISE measurement of 5.3 ± 1.3, concordant across radio and infrared surveys. The decoder enhances the quadrupole–octopole alignment probability by 11×, predicting m-dependent variance redistribution confirmed by a profiled shape test at p = 0.001 (2Δ ln ℒ = 5.7, ΔAIC = −5.7). The decoder further predicts a β̂-aligned parity axis confirmed at p = 0.009, and locates the hemispherical asymmetry axis within 16° of the predicted direction (p = 0.037). An observer-relative recording architecture can unify six large-angle cosmic anomalies. Falsifiable predictions are identified for forthcoming surveys.
Gregory O'Grady (Mon,) studied this question.