Abstract: We present an approach to pose object recognition as next token prediction.The idea is to apply a language decoder that auto-regressively predicts the text tokens from image embeddings to form labels.To ground this prediction process in auto-regression, we customize a non-causal attention mask for the decoder, incorporating two key features: modeling tokens from different labels to be independent, and treating image tokens as a prefix.This masking mechanism inspires an efficient method $-$ one-shot sampling $-$ to simultaneously sample tokens of multiple labels in parallel and rank generated labels by their probabilities during inference.To further enhance the efficiency, we propose a simple strategy to construct a compact decoder by simply discarding the intermediate blocks of a pretrained language model.This approach yields a decoder that matches the full model's performance while being notably more efficient.The code is available at [github.com/nxtp](https://github.com/kaiyuyue/nxtp).