Generalist Vision-Language Models for Fast Radio Burst detection: a zero-shot benchmark against a specialized detector

Fast Radio Burst (FRB) detection increasingly relies on specialized deep learning models that require large task-specific training sets and cannot be redefined without retraining. We evaluate whether small, open-weight, locally run generalist Vision-Language Models (VLMs) can detect FRBs in dynamic spectra under a zero-shot, prompt-only regime. On a balanced binary benchmark of 2000 simulated L-band spectra, Gemma 4 E2B reaches an accuracy of 94.05%, statistically indistinguishable from the specialized detector SwinYNet (92.85%), with a far lower false-positive rate on structured RFI (4.8% vs. 24.6%) and none on pure noise, though SwinYNet ranks perfectly (ROC-AUC 1.0000 vs. 0.9520). Rewriting the prompt alone reconfigures the same models for three-class FRB/RFI/noise classification, reaching up to 86.0% accuracy without a single false FRB while classifying each 2 s spectrum in 1.0--1.5 s, faster than the observation itself. Applied unchanged to the 1600 real FAST observations of FAST-FREX, they reject real interference almost perfectly (2 and 5 false positives in 1000 negatives) but recover only 28.5% and 27.0% of the 600 catalogued bursts, against 95.7% reported for SwinYNet on the same files. Stratifying those bursts by the dispersed signal in the image shows the limit to be the input representation rather than the classifier, recall rising to 84--85% where the sweep is unambiguous and collapsing to 1% on the 13% of positives carrying no detectable signal in a 2 s undedispersed full-band view. The simulated bursts are nearly 30 times brighter in median, and at matched brightness the recalls agree to within a few points.