Go with Your Gut: Scaling Confidence for Autoregressive Image Generation
Authors: name: Harold Haodong Chen, name: Xianfeng Wu, name: Wen-Jie Shu, name: Rongjin Guo, name: Disen Lan, name: Harry Yang, name: Ying-Cong Chen

Test-time scaling (TTS) has demonstrated remarkable success in enhancing
large language models, yet its application to next-token prediction (NTP)
autoregressive (AR) image generation remains largely uncharted. Existing TTS
approaches for visual AR (VAR), which rely on frequent partial decoding and
external reward models, are ill-suited for NTP-based image generation due to
the inherent incompleteness of intermediate decoding results. To bridge this
gap, we introduce ScalingAR, the first TTS framework specifically designed for
NTP-based AR image generation that eliminates the need for early decoding or
auxiliary rewards. ScalingAR leverages token entropy as a novel signal in
visual token generation and operates at two complementary scaling levels: (i)
Profile Level, which streams a calibrated confidence state by fusing intrinsic
and conditional signals; and (ii) Policy Level, which utilizes this state to
adaptively terminate low-confidence trajectories and dynamically schedule
guidance for phase-appropriate conditioning strength. Experiments on both
general and compositional benchmarks show that ScalingAR (1) improves base
models by 12.5% on GenEval and 15.2% on TIIF-Bench, (2) efficiently reduces
visual token consumption by 62.0% while outperforming baselines, and (3)
successfully enhances robustness, mitigating performance drops by 26.0% in
challenging scenarios.