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Yu-Chao Wang, Yanhong Lu, Yingjie Victor Chen, Tim McGraw
· 1 min read
ResearcharXiv cs.AI
MAIA: Multi-Agent Intent Articulation for Requirement Discovery in Art Commissions
arXiv:2609.12097v2 Announce Type: replace-cross
Abstract: In bespoke art commissions, laypeople know what they feel but lack the words to specify it: one participant wanted a laid-off truck driver depicted as "a ghost in his own machine" but left the medium, scale, and palette unsaid. We frame this as an articulation bottleneck at an under-served upstream stage: requirement discovery, which precedes any artist or image generator and forces the commissioner to constitute intent in the first place. We present MAIA (Multi-Agent Intent Articulation), a multi-agent system that scaffolds this stage through Socratic inquiry under a "Verification over Invention" rule, turning vague affect into a text-only brief of visual terms the user verifies. In a within-subjects study (N = 16), the full configuration produced a large, significant gain in Cognitive Support over a minimal baseline (r = 0.97, p_FDR = 0.003; LMM p_FDR < 0.001). Thematic analysis traces the same mechanism, and a validator gate structurally blocks unratified content. An exploratory blind review by three professional concept artists provides complementary descriptive evidence that the rewritten briefs were more complete and executable.
Original source
This story was published by arXiv cs.AI and written by Yu-Chao Wang, Yanhong Lu, Yingjie Victor Chen, Tim McGraw. SyncAI.news shows a preview; the complete article is on the publisher's site.
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