3 papers
cs.AI2026
DiscoverLLM: From Executing Intents to Discovering Them
Tae Soo Kim, Yoonjoo Lee, Jaesang Yu +2
To handle ambiguous and open-ended requests, Large Language Models (LLMs) are increasingly trained to interact with users to surface intents they have not yet expressed (e.g., ask…
cs.HC2026
IdeaBlocks: Expressing and Reusing Divergent Intents for Graphic Design Exploration using Generative AI
DaEun Choi, Kihoon Son, Jaesang Yu +2
While designers increasingly leverage Generative AI for divergent exploration, current interaction is optimized for convergent refinement, forcing users to specify fixed targets ra…
cs.CV2026
GUIDE: A Benchmark for Understanding and Assisting Users in Open-Ended GUI Tasks
Saelyne Yang, Jaesang Yu, Yi-Hao Peng +4
Graphical User Interface (GUI) agents have the potential to assist users in interacting with complex software (e.g., PowerPoint, Photoshop). While prior research has primarily focu…