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arXiv · 2608.07584

ComplexityWorld: Benchmarking Vision-Language Models on Verifiable Visual Decision Making

Ningxin Pan·Hanyu Li·Yehui Tang
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Abstract

Vision-language models (VLMs) have made rapid progress in visual perception and increasingly support real-world tasks that depend on images. Many such tasks, however, require more than rec- ognizing what an image contains: a model must use visual evidence to make a complete decision whose parts jointly satisfy global constraints. We introduce COMPLEXITYWORLD, a benchmark of 390 tasks across 39 domain-inspired visual worlds and 29 decision categories. Each task is generated from a hidden structured specification, rendered as a visual scene, and scored by an exe- cutable verifier that accepts any feasible solution. Under direct inference, all evaluated models ex- cept GPT-5.6-Sol remain below 40% verifier ac- ceptance rate (VAR), while GPT-5.6-Sol reaches 75.6%. Performance improves substantially when the same decision information is made explicit in structured form, yet varies sharply across equiva- lent visual presentations. Agent scaffolds provide smaller, model-dependent gains. Together, these results reveal a persistent visual-to-decision bot- tleneck that additional inference alone does not remove.

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Ningxin Pan, Hanyu Li, Yehui Tang. 2026-08-05. ComplexityWorld: Benchmarking Vision-Language Models on Verifiable Visual Decision Making. https://arxiv.org/abs/2608.07584

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