Sanyu Studio: A Multi-Agent System for Art-Historical Narrative Construction

Zhaoxi Wei; Hongye Yang; Shuyuan Tian

cs.AIAug 19, 2026

Sanyu Studio: A Multi-Agent System for Art-Historical Narrative Construction

Authors: Zhaoxi Wei, Hongye Yang, Shuyuan Tian

Organizations: Renmin University of China · Georgia Institute of Technology · University of Cologne (Universität zu Köln)

Abstract

Amid concerns that generative AI may standardize art interpretation, this paper examines whether LLM-based interaction can support plural art-historical narrative construction. We present Sanyu Studio, a multi-agent dialogue system that models 321 Sanyu oil paintings as agents with fact, interpretation, organization, and memory-filtering mechanisms. Based on a seven-day workshop with eight art-university participants, the study shows that user prompts, evidence organization, and cognitive tendencies shaped divergent yet coherent versions of digital Sanyu. The findings suggest that, under conditions of limited historical evidence, AI can amplify human agency and offer public audiences an interactive entry point into art-historical interpretation.

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Sep 10, 2026J/K move · Enter open · S save

MAIA: Multi-Agent Intent Articulation for Requirement Discovery in Art Commissions

Yu-Chao Wang, Yanhong Lu, Yingjie Victor Chen, Tim McGraw

School of Applied and Creative Computing Purdue University West Lafayette, Indiana, USA

Requirements Engineering Human-in-the-Loop AI Multi-Agent Systems Human-AI Co-Creation

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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 = 0.96, p_FDR = 0.015; LMM p_FDR < 0.001). Thematic analysis traces the same mechanism, and a validator gate structurally blocks unratified content. A complementary blind review by three professional concept artists on a sampled set of briefs corroborates this improvement from the artist's side: AI rewriting improved visual completeness and executability in all eight sampled tasks (task-level Wilcoxon p = 0.008; FDR q = 0.010), with directionally larger gains under MAIA than under the baseline (underpowered, d = 1.4-2.6).

Abstract

Human-AI Co-Creation

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