The arXiv preprint (v1, May 2026) details ATTILA, a Python-based multi-task tool combining tiling, iterative deblending, and Sérsic modeling on VEGAS g/r-band imaging of Hydra I. With a sample limited to the cluster core plus three fields, it doubles known UDGs to 48 while recovering over 80% of prior LSB galaxies—outperforming standard detection pipelines. This preprint status means findings await peer review, with key limitations including reliance on the early-type color-magnitude relation for membership, which may miss star-forming interlopers, and potential overfitting to Hydra I's dense environment. Beyond the source's claims, ATTILA addresses a systemic observational bias: traditional surveys like SDSS undercount faint structures by factors of 2-3, skewing halo mass scaling relations used in dark matter mapping. Cross-referencing with van Dokkum et al. (2015, ApJL) on Coma UDGs and Zaritsky et al. (2023, AJ) on automated LSB pipelines reveals ATTILA's edge in blending mitigation but highlights missed opportunities for multi-wavelength validation. By enabling fuller LSB censuses, it could refine simulations of galaxy assembly in clusters, though broader application to field environments remains untested.