VQ-VA World: Towards High-Quality Visual Question-Visual Answering

Chenhui Gou; Zilong Chen; Zeyu Wang; Feng Li; Deyao Zhu; Zicheng Duan; Kunchang Li; Chaorui Deng; Hongyi Yuan; Haoqi Fan; Cihang Xie; Jianfei Cai; Hamid Rezatofighi

arXiv · 2511.20573

VQ-VA World: Towards High-Quality Visual Question-Visual Answering

Chenhui Gou·Zilong Chen·Zeyu Wang·Feng Li·Deyao Zhu·Zicheng Duan·Kunchang Li·Chaorui Deng·Hongyi Yuan·Haoqi Fan·Cihang Xie·Jianfei Cai·Hamid Rezatofighi

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Abstract

This paper studies Visual Question-Visual Answering (VQ-VA): generating an image, rather than text, in response to a visual question -- an ability that has recently emerged in proprietary systems such as NanoBanana and GPT-Image. To also bring this capability to open-source models, we introduce VQ-VA World, a data-centric framework built around an agentic pipeline for large-scale, targeted data construction. Leveraging web-scale deployment, this pipeline crawls a massive amount of ~1.8M high-quality, interleaved image-text samples for model training. For evaluation, we further release IntelligentBench, a human-curated benchmark that systematically assesses VQ-VA along the aspects of world knowledge, design knowledge, and reasoning. Training with VQ-VA World data yields strong empirical gains: it helps LightFusion attain 53.06 on IntelligentBench, substantially surpassing the best prior open-source baselines (i.e., 7.78 from vanilla LightFusion; 1.94 from UniWorld-V1), and significantly narrowing the gap toward leading proprietary systems (e.g., 81.67 from NanoBanana; 82.64 from GPT-Image). By releasing the full suite of model weights, datasets, and pipelines, we hope to stimulate future research on VQ-VA.

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Chenhui Gou, Zilong Chen, Zeyu Wang, Feng Li, Deyao Zhu, Zicheng Duan, Kunchang Li, Chaorui Deng, Hongyi Yuan, Haoqi Fan, Cihang Xie, Jianfei Cai, Hamid Rezatofighi. 2025-11-25. VQ-VA World: Towards High-Quality Visual Question-Visual Answering. https://arxiv.org/abs/2511.20573

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