Beneath the stunning visual fidelity of modern AIGC models lies a "logical desert", where systems fail tasks that require physical, causal, or complex spatial reasoning. Current evaluations largely rely on superficial metrics or fragmented benchmarks, creating a ``performance mirage'' that overlooks the generative process. To address this, we introduce ViGoR Vision-G}nerative Reasoning-centric Benchmark), a unified framework designed to dismantle this mirage. ViGoR distinguishes itself through four key innovations: 1) holistic cross-modal coverage bridging Image-to-Image and Video tasks; 2) a dual-track mechanism evaluating both intermediate processes and final results; 3) an evidence-grounded automated judge ensuring high human alignment; and 4) granular diagnostic analysis that decomposes performance into fine-grained cognitive dimensions. Experiments on over 20 leading models reveal that even state-of-the-art systems harbor significant reasoning deficits, establishing ViGoR as a critical ``stress test'' for the next generation of intelligent vision models. The demo have been available at https://vincenthancoder.github.io/ViGoR-Bench/
@article{arxiv.2603.25823,
title = {ViGoR-Bench: How Far Are Visual Generative Models From Zero-Shot Visual Reasoners?},
author = {Haonan Han and Jiancheng Huang and Xiaopeng Sun and Junyan He and Rui Yang and Jie Hu and Xiaojiang Peng and Lin Ma and Xiaoming Wei and Xiu Li},
journal= {arXiv preprint arXiv:2603.25823},
year = {2026}
}