The arXiv preprint (v1, June 2026) introduces a vector magnetometry approach for nitrogen-vacancy centers that replaces conventional optically detected magnetic resonance with two orthogonally polarized broadband microwave pulses whose transmitted spectra encode Zeeman splitting across all three magnetic-field components. By training deep neural networks on simulated transmission data the authors report component sensitivities from 5 to 100 pT/√Hz and roughly 1 nT accuracy at 70 dB SNR, eliminating the usual bias-field requirement above Earth’s field. Because the work remains entirely simulated, no experimental sample size, diamond NV density, or microwave delivery hardware details are provided; the claimed performance therefore rests on idealized noise models that omit strain inhomogeneity, temperature drift, and actual pulse-shaping imperfections routinely encountered in laboratory NV setups. Earlier experimental demonstrations of broadband microwave vector sensing (e.g., the 2023 Nature Communications work by Schloss et al. using frequency-comb readout) achieved comparable vector resolution but required a 30 mT bias field; the new method’s elimination of that constraint could directly benefit unshielded navigation and low-field medical magnetoencephalography once hardware validation occurs. A third related study (Phys. Rev. Applied 2024 on NV-based current imaging) showed that neural-network post-processing can recover sub-nT accuracy from noisy spectra, supporting the authors’ optimism, yet also highlighted a 3× degradation when moving from simulation to a real 4-NV ensemble. The preprint therefore correctly identifies a promising microwave architecture, but its one-year translation timeline hinges on rapid bench-top experiments that have not yet been reported.