AI画图不懂的，它开始学会搜了
现在的AI画图工具，你让它画个冷门角色或刚出的新闻人物，它会自信地画个错的——因为它训练时没见过。这篇论文发现，当前最强开源模型在覆盖真实世界知识的测试里只拿了21到28分（满分100），比现有基准低了40分。问题出在：AI不知道自己的知识边界在哪，硬画不如不画。研究者搞了个「先教后搜」的框架：先让AI自己画，画不出来的再去搜，搜回来的信息再用来画。这样AI慢慢学会区分「我懂的」和「我得去查的」。虽然还不是你明天能用的产品，但它指了一条路：让AI画图不再瞎编，而是像人一样，不懂就去查。
📄 原文摘要(英文)
Visual generators excel at rendering, but they confidently fabricate what they do not know. User requests are unbounded, evolving, and deeply long-tailed: new characters, trending entities, post-cutoff events, and more. This world-knowledge bottleneck is structural: generators are trained on fixed corpora, but the visual world is open-ended. We construct SearchGen-20K and SearchGen-Bench, with 20,839 prompts spanning twelve failure categories and twenty-two domains, paired with a pre-executed multimodal SearchGen-Corpus-1M to support offline, reproducible research. On SearchGen-Bench, frontier open generators score only 21 to 28 out of 100, a 40-point collapse invisible to existing benchmarks. The natural remedy is to employ search tools, enabling agentic visual generation. However, we find that naive search fails: it retrieves indiscriminately, injecting noise into prompts the generator already handles. We trace the root cause to a generator-specific, evolving knowledge boundary: the divide between what a generator can internalize through training and what must remain in external context. Although this boundary is hard to specify in advance, we show that it is discoverable through a teach-then-search co-training framework. Even a minimal version of this co-training recipe produces monotonic improvement, laying the foundation for recursive self-improvement in visual generation that can meet world-knowledge-grounded requests. We release the full dataset, co-training corpus, and search corpus as a replayable harness for tool-augmented, world-knowledge-grounded visual generation.