Grid, Set, or Graph? Representations for Learned VLBI Reconstruction Under Distribution Shift

Radio interferometers measure complex visibilities at irregularly sampled spatial frequencies, with sampling patterns that vary between observations. We investigate how the representation of these measurements affects learned image reconstruction by comparing three approaches: a regular grid processed by a convolutional neural network (CNN), an unordered set processed by DeepSets, and a $k$-nearest-neighbour graph processed by a graph neural network (GNN). The models are trained to reproduce CLEAN images from Astrogeo X-band observations and evaluated without adaptation on the MOJAVE 15 GHz survey. The GNN achieves the lowest median normalized mean squared error (NMSE) on both held-out X-band observations (0.202, compared with 0.228 for DeepSets and 0.376 for the CNN) and MOJAVE (0.386, compared with 0.448 and 0.707). However, all three models exhibit similar relative increases in NMSE under the survey shift (88–96\%), providing no clear evidence of differences in relative robustness. An oracle-scaled mean-image baseline outperforms all three models, while diagnostics on selected observations suggest that amplitude underprediction contributes substantially to the degradation on MOJAVE. Because the survey shift simultaneously changes observing frequency, array configuration, and source characteristics, these results do not isolate the effects of sampling geometry.