VQ-VA World: Towards High-Quality Visual Question-Visual Answering
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.
69266e53243b2216fb75ca64
Chenhui Gou
69266e53243b2216fb75ca65
Zilong Chen
69266e53243b2216fb75ca66
Zeyu Wang
69266e53243b2216fb75ca67
Feng Li
69266e53243b2216fb75ca68
Deyao Zhu
69266e53243b2216fb75ca69
Zicheng Duan
claimed_verified
2025-11-26T09:27:35.069Z
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/avatars/86305d1eac3917bf9859ac0ddbb4e845.svg
Zicheng Duan
ZichengD
user
ZichengD
69266e53243b2216fb75ca6a
Kunchang Li
69266e53243b2216fb75ca6b
Chaorui Deng
69266e53243b2216fb75ca6c
Hongyi Yuan
69266e53243b2216fb75ca6d
Haoqi Fan
69266e53243b2216fb75ca6e
Cihang Xie
69266e53243b2216fb75ca6f
Jianfei Cai
69266e53243b2216fb75ca70
Hamid Rezatofighi
VQ-VA World: Towards High-Quality Visual Question-Visual Answering
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.
A data-centric framework and benchmark for Visual Question-Visual Answering (VQ-VA) improve open-source model performance, narrowing the gap with proprietary systems.
Visual Question-Visual Answering VQ-VA agentic pipeline web-scale deployment image-text samples IntelligentBench world knowledge design knowledge reasoning LightFusion UniWorld-V1 NanoBanana GPT-Image