arXiv:2607. 23126v1 Announce Type: cross Abstract: Generative AI design tools make natural-language prompts a starting point for design, placing new articulation demands on designers.
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arXiv:2607. 23126v2 Announce Type: replace-cross Abstract: The rapid adoption of generative AI tools has created new literacy demands for designers who must verbalize tacit knowledge through natural language prompts.
The paper introduces a new inference architecture for vibe design agents that separates design exploration from implementation. By generating structured design specifications with typicality scores and selecting one for downstream generation, the method allows users to explore coherent UI alternatives without altering the underlying generation settings. Experiments on UI themes and visual-asset prompts show increased selection coverage and screenshot variation, with mixed preferences from an LLM judge and modest operational costs in a large online test.
Generative AI tools for creative work tend to be designed around the goal of removing friction, on the assumption that smoother iteration and faster output translate into more value for the designer. We argue, however, that this framing leaves out something important about how design ideation works, namely reflection-in-action.
arXiv:2607. 26827v1 Announce Type: cross Abstract: Generative AI tools for creative work tend to be designed around the goal of removing friction, on the assumption that smoother iteration and faster output translate into more value for the designer.
arXiv:2607. 07521v1 Announce Type: cross Abstract: AI agents that generate final answers based on user input often do not meet the needs of creative fields.
AI agents that generate final answers based on user input often do not meet the needs of creative fields. Fields such as structural design and architecture need interactive systems that help users externalise and develop ideas, explore alternatives, and refine partial solutions.