The paper examines how large language model (LLM) based graphical user interface (GUI) agents respond to digital nudges. Using a randomized online shopping experiment with 3,600 agents across six frontier models, it finds that agents are vulnerable to both automatic and reflective nudges. The study shows that the agents’ reasoning configuration moderates these effects in opposite directions—reducing susceptibility to automatic nudges while increasing it to reflective social influence nudges—and that this redirection is systematically linked to model scale.
arXiv:2609.16436v1 Announce Type: cross
Abstract: Simulations based on large language models (LLMs) have proven to be powerful for understanding human behavior, making them valuable additions to the...
By Jiayue Gaveal Fan, Arul Murugan, Shreyas Krishnan, Abhishek Nagaraj
arXiv:2603. 23433v3 Announce Type: replace Abstract: AI agents are becoming active decision-makers on the Internet.
By Giulio Frey, Kawin Ethayarajh
arXiv:2608. 09278v1 Announce Type: cross Abstract: GUI agents have advanced rapidly, producing a growing body of frameworks, benchmarks, and applications.
By Shengcheng Yu, Yuchen Ling, Junyang Xing, Quan Zhou, Chunrong Fang, Zhenyu Chen
arXiv:2608.29464v1 Announce Type: cross
Abstract: Chain-of-thought (CoT) monitoring assumes that reasoning traces faithfully record the information that shapes a model's answer. Existing faithfulness...
By Aryo Pradipta Gema, Neel Rajani, Rohit Saxena, Wai-Chung Kwan, Pasquale Minervini
arXiv:2608.21841v1 Announce Type: new
Abstract: Conversational AI increasingly shapes consequential decisions, yet users have limited support for recognizing and resisting manipulation. We present AI...
By Rachel Poonsiriwong (Pub), Chayapatr (Pub), Archiwaranguprok, Constanze Albrecht, Monchai Lertsutthiwong, Pattie Maes, Pat Pataranutaporn