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arXiv · 2606.07861

The Last Visible Pixel: Probing Fine-Scale Perception in Vision-Language Models

Lujun Li·Lama Sleem·Niccolo Gentile·Yangjie Xu·Yewei Song·Wenbo Wu·Radu State
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Abstract

Recent vision-language models (VLMs) excel at multimodal understanding and reasoning, yet their fine-grained visual perception remains underexplored. A natural extension of ``How many r are there in Strawberry?'' asks: how small a visual pattern can a VLM reliably perceive? As such, we introduce FineSightBench, a new benchmark that systematically probes this limit by separating perception tasks (pixel-level recognition of letters, shapes, objects) from reasoning tasks (spatial reasoning, counting, ordering over small targets) across controlled scales of 4--48px. Through comprehensive experiments and detailed failure mode analysis on state-of-the-art models, we reveal a sharp dissociation: perception saturates around 12px, while reasoning remains limited even at larger scales, with persistent numeracy and sequence errors. These findings expose fundamental deficiencies in VLMs' fine-scale visual reasoning that demand more rigorous evaluation.

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Lujun Li, Lama Sleem, Niccolo Gentile, Yangjie Xu, Yewei Song, Wenbo Wu, Radu State. 2026-06-05. The Last Visible Pixel: Probing Fine-Scale Perception in Vision-Language Models. https://arxiv.org/abs/2606.07861

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