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State-of-the-art generative AI as of early 2026 produces synthetic media that defeats every major single-modality detection paradigm. Pixel-artifact detectors, GAN fingerprinting classifiers, and physiological signal analyzers collectively achieve only 6-18% accuracy against current-generation diffusion models, NeRF, and video generation architectures, with 50% AUC degradation between controlled benchmarks and real-world deployment. Meanwhile, the EU AI Act Article 50 (effective August 2, 2026) mandates synthetic media detectability, creating immediate regulatory urgency. The fundamental limitation of existing approaches is architectural: they treat content verification as a pattern recognition problem over surface features of a single modality. A generative model can learn to produce plausible pixels, plausible audio, or plausible motion in isolation. But it cannot simultaneously satisfy the cross-domain physical constraints that a real human body imposes on its environment. A living person generates correlated physical consequences across optics (shadow geometry governed by solar position), motion (respiratory displacement measurable in chest movement, audio formants, and RF micro-Doppler), geomagnetism (device magnetometer readings consistent with geographic declination), meteorology (scene appearance consistent with archived weather records), and scene geometry (3D body volume and gait dynamics)—all governed by independent physical laws. We present a verification architecture that shifts the question from 'does this content look real?' to 'are the cross-domain physical constraints satisfied?' The system constructs, for each content item, a Unified Typed Evidence Graph (UTEG) fusing physics-constrained signals from ten independent sensing domains across seven evidence layers, processed by ten autonomous specialist agents with graph-wide hard-veto propagation. Any single physics violation collapses the authenticity score to zero, regardless of all other evidence. Key contributions and results: (1) Weather State Coherence Layer (WSCL): First content authentication system to cross-validate scene-derived weather features (precipitation, cloud cover, atmospheric haze, surface wetness) against public meteorological archives without dedicated sensing hardware. Achieves 91.3-94.6% GPS-spoof detection at 1.2% false-positive rate across 8 geographic regions and 12 months of archive data. (2) Device Sensor Coherence Layer (DSCL): Verifies magnetometer readings across four dimensions including magnetic declination consistency via IGRF/WMM for claimed location. Three-order-of-magnitude variance separation between authentic (0.8-12.4 uT, median 3.7) and artificial data (0.001-0.05 uT, median 0.008). 93.8% detection at 0.7% false-positive rate. (3) Persistent Spectral-Topological Sheaf Intelligence (PSTSI): Addresses three critical limitations of standard persistent homology via persistent topological Laplacians and persistent sheaf Laplacians. Detects 94.7% of topology-preserving geometric distortion attacks invisible to standard persistent homology (3.1% baseline detection). (4) Bio-Entangled RF Lock: Cross-spectral coherence between optical respiratory motion and RF micro-Doppler phase achieves 99.7% true-negative rate on synthetic content (0.3% false-positive). Authentic content coherence 0.72-0.98; synthetic 0.00-0.04. (5) Normalized Compression Distance (NCD): Zero-shot detection of AI-generated content across five unseen generative architectures achieves 89.3% accuracy at 5% FPR without retraining. Human NCD 0.71-0.94; AI-generated 0.31-0.62. Evaluation across 15,000 adversarially crafted items including 2,000 evasion attempts, 8 geographic regions, 12 months of meteorological data, and five generative architectures (two diffusion, one NeRF, one GAN, one video generation) validates all performance claims. The integrated ten-domain system substantially exceeds any partial configuration. This work extends the indicator-based validation architecture of U.S. Patent No. 11,301,910 B2 (System and Method for Validating Video Reviews, Melini LLC, 2022) to ten physics-constrained sensing domains with hard-veto enforcement. It is complementary to the companion bias detection system, cryptographic bias provenance system, and AI memory poisoning detection system disclosed in related Melini LLC publications. Patent Notice: The systems, methods, and architectures described in this paper are the subject of pending U.S. patent applications filed with the USPTO, including MELINI-10878-004PV2, U.S. Provisional Application No. 63/999,514 (filed March 8, 2026), with priority from U.S. Provisional Application Serial No. 63/946,791 (December 22, 2025), and related co-pending applications MELINI-10878-006PV1 (Feb 12, 2026), MELINI-10878-007PV1 (Feb 19, 2026), and MELINI-10878-008PV1 (Feb 20, 2026), all assigned to Melini LLC. This paper constitutes a disclosure under 35 U.S.C. 102(b)(1)(A). All patent rights reserved. Related Granted Patent: U.S. Patent No. 11,301,910 B2. License: CC BY-NC-ND 4.0 International.