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). ...
| Authors | Raiff H. Santos, Amilcar R. Queiroz, Tharcisyo S. S. Duarte et al. |
|---|---|
| Primary category | cs.LG |
| Published | 8 July 2026 |
| Last updated at source | 20 August 2026 |
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