{"ID":2837098,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.20573","arxiv_id":"2511.20573","title":"VQ-VA World: Towards High-Quality Visual Question-Visual Answering","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.","short_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-cent...","url_abs":"https://arxiv.org/abs/2511.20573","url_pdf":"https://arxiv.org/pdf/2511.20573v1","authors":"[\"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\"]","published":"2025-11-25T18:06:22Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}