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Life sciences · Preprint
arXiv · September 10, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is an unrefereed technical contribution proposing a lightweight autoregressive module (Logit Refiner) to improve Visual Autoregressive Models by restoring spatial dependencies among same-scale tokens during image generation. The authors report consistent improvement across multiple model scales on ImageNet 256×256 and text-to-image tasks, but the work has not undergone peer review and lacks comparative benchmarking against established baselines.
Methods paper with controlled ablations; preprint. Visual autoregressive models of varying sizes (310M to 2B parameters); tested on image generation benchmarks.. Intervention: Logit Refiner: lightweight autoregressive module performing sequential token sampling conditioned on frozen backbone features. Compared with: Baseline VAR models without the Logit Refiner; ablated variants without joint intra-scale sampling.
Logit Refiner adds ~10% parameters and less than 5% of base model training compute to pretrained VAR checkpoints without retraining On class-conditional ImageNet 256×256, a 1.1B-parameter model with Logit Refiner surpasses one twice its size Method generalizes from class-conditional to text-to-image generation, suggesting mean-field bottleneck is common across VAR variants
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Unreviewed technical report presenting a novel method for improving image generation, with controlled experiments on standard benchmarks but lacking peer review and clinical/real-world validation.
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Visual Autoregressive Models (VAR) generate images through next-scale prediction, producing all tokens within each scale in parallel. We show that this parallel decoding constitutes a mean-field-style approximation that discards spatial dependencies among same-scale tokens, causing locally incoherent samples regardless of backbone capacity -- a limitation of the decoding rule. Addressing this limitation, we introduce the Logit Refiner, a lightweight autoregressive module that restores intra-scale dependencies by sequentially sampling tokens conditioned on frozen backbone features. Adding only ~10% parameters and less than 5% of the base model's training compute, it plugs into any pretrained VAR checkpoint without retraining. Controlled ablations isolate joint intra-scale sampling -- rather than additional capacity or training -- as the critical ingredient. Across backbones from 310M to 2B parameters on class-conditional ImageNet 256x256, the refiner consistently improves generation quality, enabling a 1.1B-parameter model to surpass one twice its size. The approach further generalizes to text-to-image generation, confirming that the mean-field bottleneck persists across VAR variants and is effectively alleviated by our method. Project page: https://compvis.github.io/logit-refiner/
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