BannerAgency: Advertising Banner Design with Multimodal LLM Agents

Challenge:	Advertising banners are an instrumental medium in digital marketing campaigns.
Approach:	They propose a training-free framework for fully automated banner ad design creation that enables frontier multimodal large language models to streamline the production of effective banners with minimal manual effort.
Outcome:	The proposed framework is based on a training-free model that can be used to create fully automated banner ad design creations with minimal manual effort across diverse marketing contexts.

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Overview

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Zhao Wang, Bowen Chen, Yotaro Shimose, Sota Moriyama, Heng Wang, Shingo Takamatsu

Multimodality

Challenge:	Recent advances in generative modeling have greatly improved image synthesis quality.
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BannerBench: Benchmarking Vision Language Models for Multi-Ad Selection with Human Preferences (2025.findings-emnlp)

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Hiroto Otake, Peinan Zhang, Yusuke Sakai, Masato Mita, Hiroki Ouchi, Taro Watanabe

Semantic Text Processing

Challenge:	Web banner advertisements are often selected manually because of human preferences . a new benchmark evaluates the degree of alignment with human preferences in two tasks .
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AdaptAgent: Adapting Multimodal Web Agents with Few-Shot Learning from Human Demonstrations (2025.acl-long)

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Gaurav Verma, Rachneet Kaur, Nishan Srishankar, Zhen Zeng, Tucker Balch, Manuela Veloso

Language Models

Challenge:	State-of-the-art multimodal web agents can perform many web tasks by processing user instructions and interacting with graphical user interfaces (GUIs).
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SMART-Editor: A Multi-Agent Framework for Human-Like Design Editing with Structural Integrity (2026.findings-eacl)

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Ishani Mondal, Meera Bharadwaj, Ayush Roy, Aparna Garimella, Jordan Boyd-Graber

Visual Data in NLP

Challenge:	SMART-EDITOR is a framework for compositional layout and content editing for structured visual domains.
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KAFA: Rethinking Image Ad Understanding with Knowledge-Augmented Feature Adaptation of Vision-Language Models (2023.acl-industry)

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Zhiwei Jia, Pradyumna Narayana, Arjun Akula, Garima Pruthi, Hao Su, Sugato Basu, Varun Jampani

Semantic Text Processing

Challenge:	Image ad understanding is a crucial task with wide real-world applications, but is under-explored in the machine learning community due to the lack of foundational vision-language models (VLMs) .
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The Revolution of Multimodal Large Language Models: A Survey (2024.findings-acl)

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Davide Caffagni, Federico Cocchi, Luca Barsellotti, Nicholas Moratelli, Sara Sarto, Lorenzo Baraldi, Lorenzo Baraldi, Marcella Cornia, Rita Cucchiara

Multimodality

Challenge:	Recent advances in large language models have led to the development of multimodal large language model.
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ChartEdit: How Far Are MLLMs From Automating Chart Analysis? Evaluating MLLMs’ Capability via Chart Editing (2025.findings-acl)

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Xuanle Zhao, Xuexin Liu, Yang Haoyue, Xianzhen Luo, Fanhu Zeng, Jianling Li, Qi Shi, Chi Chen

Multimodality

Challenge:	Existing evaluations of multimodal large language models rely on limited case studies . however, they lack the ability to generate accurate edits according to the instructions .
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Towards Unified Multimodal Large Language Models: A survey (2026.findings-acl)

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Xu Ma, Yitian Zhang, Yun Fu

Semantic Text Processing

Challenge:	unified multimodal large language models (MLLMs) are emerging but lack a systematic framework to connect them and situate current trends within a broader landscape.
Approach:	They present a systematic review of unified Multimodal Large Language Models . they outline the foundational concepts and prerequisites for understanding them .
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Unveiling Multimodal Processing: Exploring Activation Patterns in Multimodal LLMs for Interpretability and Efficiency (2025.findings-emnlp)

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Chuan Wu, Meng Su, Youxuan Fang, Shaolin Zhu

Responsible & Trustworthy NLP

Challenge:	Recent advances in multimodal large language models have remained opaque.
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Learning from LLM Agents: In-Context Generative Models for Text Casing in E-Commerce Ads (2025.emnlp-industry)

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Yingxue Zhou, Tan Zhu, Tao Zeng, Zigeng Wang, Wei Shen

Reasoning

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