Visual autoregressive (VAR) models offer fast, high-quality text-to-image generation but still fail at attribute binding and spatial relations. Existing VAR test-time methods search sampled trajectories or apply generic logit guidance but do not directly optimize compositional constraints; gradient-based diffusion methods do not transfer to stateful, discrete, multi-resolution VAR sampling. We introduce $\textbf{VISTA}$ ($\textbf{Vi}$sual Autoregressive $\textbf{S}$emantic $\textbf{T}$est-time $\textbf{A}$lignment), the first gradient-based test-time framework for compositional alignment in next-scale autoregressive image generation. Built on Infinity, VISTA optimizes intermediate representations through the frozen transformer without parameter updates or training. It stabilizes optimization across scales and exposes a common interface into which any differentiable constraint on cross-attention can be plugged. Across two benchmarks and model scales, VISTA improves every targeted category, raising the mean by nearly 20% on a 2B backbone and almost 6% on an 8B backbone, especially on spatial relations. In human evaluation, VISTA wins 83% of decisive compositionality comparisons by plurality vote, with no significant preference in perceived quality; independently, ImageReward scores its outputs 20% higher. VISTA enables the 2B model to surpass a backbone four times its size, showing much of the compositional gap is recoverable at test time.

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