arXiv:2607. 23696v1 Announce Type: new Abstract: Ad creative optimization is increasingly constrained by evaluation rather than generation.
By Kevin Lee, Benjamin Letham, Zhiyuan Jerry Lin, Elodie Samson, Eric Onofrey, Poppy Zhang, Shawndra Hill, Eytan Bakshy
arXiv:2603. 11863v2 Announce Type: replace Abstract: The saturation of high-quality pre-training data has shifted research focus toward evolutionary systems capable of continuously generating novel artifacts, leading to the success of AlphaEvolve.
By Zi-Han Wang, Lam Nguyen, Zhengyang Zhao, Mengyue Yang, Chengwei Qin, Yujiu Yang, Linyi Yang
arXiv:2606. 31711v1 Announce Type: new Abstract: Faithfulness -- how precisely a generated image aligns with its prompt -- is increasingly central to the real-world utility of text-to-image (T2I) models.
By Yuanhao Ban, Tong Xie, Sohyun An, Yunqi Hong, Evan Frick, I-Hung Hsu, Wei-Lin Chiang, Ion Stoica, Cho-Jui Hsieh
arXiv:2607. 20528v1 Announce Type: new Abstract: Online recommendation platforms increasingly use Large Language Models (LLMs) to extract structured features from ad creatives.
By Sebastian Koralewski, Merwan Barlier, Yulia Stolin, Bla\v{z} \v{S}krlj
CommerceVibe is a system that generates e‑commerce creatives by synthesizing executable HTML/CSS code conditioned on product images, design requirements, and product information. It uses dual‑feedback reinforcement learning, combining rule‑based checks for text readability, product visibility, and layout validity with visual feedback from a vision‑language model that evaluates perceptual and commercial aspects. After fine‑tuning a large language model on 28,000 examples and applying dual‑feedback reinforcement learning, CommerceVibe achieves a weighted score of 94.0/100 on a 1,300‑case benchmark, outperforming both its SFT‑only counterpart and external models, and is validated by expert blind evaluations.
By Yajiao Xu, Jin Zhang, Jiangbo Ai, Tao Jiang, Mo Xu, Lina Huang, Chengfu Huo
Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in their creative potential. Realizing this potential requires systematic and scalable methods for evaluating creativity across diverse tasks.