The paper investigates whether Multimodal Large Language Models (MLLMs) can reason about visual persuasion. It finds that current models often shortcut by citing obvious visual elements instead of truly reasoning about diverse cues, and proposes a fine‑tuning method using multi‑perspective rationales along with an evaluation framework that separates prediction performance from rationale faithfulness. The study shows that while fine‑tuning improves prediction, higher accuracy does not guarantee more faithful reasoning, underscoring the need for distinct evaluation of effectiveness and faithfulness.
By Naeun Lee, Hyunjong Kim, Sunghwan Choi, Injin Kong, Yohan Jo
Marketers now deploy generative AI agents as synthetic consumers to pretest visual assets such as logos, packaging, and advertising at a fraction of human-panel cost. However, this procedure assumes t...
arXiv:2609.22942v1 Announce Type: new
Abstract: Generative models are rapidly expanding image quality assessment (IQA) beyond traditional fidelity factors to emerging dimensions such as physical plau...
By Zhenchen Tang, Bo Peng, Zichuan Wang, Songlin Yang, Leilei Cao, Fengjie Zhu, Jing Dong
AromaGen is a 12‑channel wearable system that translates free‑form natural‑language descriptions into olfactory experiences by selecting from 12 base odorants that span a semantically derived olfactory space. The system supports iterative refinement via natural‑language feedback. In a study with 60 participants and 50 benchmark smells, users could identify AromaGen‑generated scents above chance, and AI‑generated compositions performed comparably to human experts, with feedback improving perceived similarity.
By Awu Chen, Silang Wang, Yunge Wen, Jianing Yu, Zinab Eisa, Ana Julia Santos Lopes, Jas Brooks, Hiroshi Ishii, Paul Pu Liang
arXiv:2605.14542v2 Announce Type: replace
Abstract: A skilled live-commerce host is not merely a narrator, but a sales agent who converts viewer curiosity into purchase intent through expert product...
By Yuyan Chen
AgenticGen is a reward‑guided framework for generating advertising videos that splits the task into strategy selection and draft generation, allowing online business feedback to supervise each stage. It learns performance‑based rewards from accumulated online metrics and rubric‑based rewards aligned with human quality standards, then applies DPO and GRPO to refine policies. Offline tests confirm the reward models, and online A/B tests on TikTok show significant gains in CTR, CVR, and advertising value over a baseline.
By Xingyuan Bu, Chengru Song, Hao Zhou, Tao Zhou, Dong Li, Wei Li, Shilong Li, Hao Shi, Yongxin Guo, Donghao Zhou, Qiangpeng Yang, Shilei Wen