A small non-autoregressive yes/no visual question answering model built for research on calibrated decisions. It answers a yes/no question about an image in one forward pass, with no generation and no verbalised confidence. The confidence is the softmax over two token logits followed by a single post-hoc temperature. - Image. A frozen SigLIP2-base encodes the image into its pooled vector (pooleroutput, 768-d). - Prefix token. That vector becomes one prefix token, prepended to the input embeddings of ModernBERT-base: - anchor is a learned token, initialised from the " image" embedding. - alpha is a scalar gate, initialised to 0, so the model starts as exactly the text-only model. - gmax caps…