Aug 2026· 1 citation· ⚡ 1 influential· 54 references
Computer Science
TL;DR
FocusGen is presented, an interactive system that introduces external perspectives into visual design exploration through a "virtual focus group" of simulated persona agents, and is positioned as a divergence scaffold for early-stage ideation rather than a substitute for audience research.
Abstract
Creative professionals rarely design for themselves--they design for audiences whose preferences they must anticipate. Yet current text-to-image exploration tools derive diversity entirely from the designer's own input--their prompts, their chosen dimensions, their search queries--confining exploration to what the designer already knows to look for. We present FocusGen, an interactive system that introduces external perspectives into visual design exploration through a"virtual focus group"of simulated persona agents. In contrast to prior persona systems in which multiple agents converge as critics on a single evolving artifact, FocusGen uses personas as parallel generators: each agent--constructed from demographic data, a procedurally generated backstory, and aesthetic preferences elicited through interviews--independently drives an iterative generation loop that produces its own visual concept, transforming one design brief into a spectrum of audience-conditioned directions. With real human participants, we confirm that the iterative refinement loop produces outputs people prefer over zero-shot generation. With synthetic agents at scale, we show that persona conditioning yields higher visual diversity than a generic-assistant baseline--measured by CLIP distance and corroborated by human perceptual judgments--and that open-ended preference interviews yield more diverse outputs than structured ones for both human and synthetic cohorts, while also revealing that agent cohorts recover only part of the diversity of comparable human cohorts. A qualitative study with 16 creative professionals suggests FocusGen helps designers discover unanticipated directions, overcome fixation, and probe audience contexts--while surfacing stereotyping risks that we analyze. We position FocusGen as a divergence scaffold for early-stage ideation rather than a substitute for audience research.
This work identifies structured design specifications as a practical control point for exploring alternative UI concepts while keeping downstream generation settings fixed through an inference architecture that makes design direction an explicit intermediate decision.
Yi-Fan Zhang, N. Bui, Georgios Evangelopoulos et al.· 0 citations
C cultivation is proposed : a design metaphor in which LLM-generated branches are stored as persistent material for authors to shape through iterative curation, and reflects on how this metaphor reframes human-AI creative collaboration: authors become garden-ers, tending ever-growing branches rather than constraining e...
Understanding how diverse audiences engage with narratives and content is central to exhibition design, yet designers often rely on intuition. Existing experience evaluation methods are typically retrospective, costly, and offer limited access to visitors'internal states, hindering early-stage iterative refinement. Rat...
Huanchen Wang, Qiuming Chen, Zhong-Hao Ji et al.· 0 citations
This paper investigates the role of generative AI as a co-creative design partner with children (aged 9-12) to envision child-centric school streets. Drawing on future studies that highlight how diverse ways of imagining shape human perception, we examine how AI-mediated speculation reshapes co-design & participatory d...
C. Tandon, Charu Monga· International Conference on...· 0 citations
Designers require different design spaces across creative stages: broad during exploration, and targeted during refinement. Yet existing agent-driven tools assume a fixed or continuously expanding space, leaving designers to manage and navigate it themselves. Informed by a formative study with five designers, we propos...
Yuzhe You, G. Chan, Shu-Nan Guo et al.· 0 citations
Interactive visual interfaces have become an important means of controlling generative image models, enabling users to manipulate generation through prompts, direct manipulation, and a range of interactions. However, existing techniques are typically presented as independent systems, making it difficult to understand h...
Susie S. Y. Li, Mingwei Li, Remco Chang· 0 citations
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduSep 30, 2026
Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.