Recent advances in software and data-processing strategies have transformed the capabilities of the LOw Frequency ARray (LOFAR), enabling wide-field radio imaging at unprecedented depth and angular resolution. In this talk, I will present the developments that have made ultra-deep sub-arcsecond imaging with LOFAR both computationally feasible and scientifically transformative.

I will describe improvements to the LOFAR-VLBI imaging pipeline (PILOT), including automated self-calibration driven by signal-to-noise metrics, machine-learning-based image quality assessment, and optimised processing strategies that reduce computational costs by factors of 4-6 compared to previous approaches. To demonstrate these capabilities, we applied the updated pipeline to 200 hours of LOFAR High-Band Antenna observations of the ELAIS-N1 field, which corresponds to processing 400 TB of visibility data. The resulting image reaches a central sensitivity of 5.6 μJy beam⁻¹ at 0.3 arcsec resolution over a 2.5×2.5 deg² field, making it the deepest radio image below 3 GHz and one of the deepest radio images ever produced. This dataset opens a new window on the faint radio-source population, from distant active galactic nuclei to some of the earliest star-forming galaxies. More broadly, it demonstrates how advances in calibration, automation, and computational efficiency are paving the way for the next generation of ultra-deep radio surveys with LOFAR 2.0 and eventually the Square Kilometre Array (SKA).