# Conducting Stylistic Analysis of Paintings through an Art-History Agent

arXiv: https://arxiv.org/abs/2608.29644 · HTML: https://mlsanity.com/paper/2608.29644/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 30 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet](https://mlsanity.com/paper/2605.29358/) (2026)
- [Language agents achieve superhuman synthesis of scientific knowledge](https://mlsanity.com/paper/2409.13740/) (2024)
- [Interpreting CLIP with Sparse Linear Concept Embeddings (SpLiCE)](https://mlsanity.com/paper/2402.10376/) (2024)
- [XAIxArts Manifesto: Explainable AI for the Arts](https://mlsanity.com/paper/2502.21220/) (2025)
- [Art Authentication with Vision Transformers](https://mlsanity.com/paper/2307.03039/) (2023)
- [Sparse Subspace Clustering for Concept Discovery (SSCCD)](https://mlsanity.com/paper/2203.06043/) (2022)
- [Synthetic images aid the recognition of human-made art forgeries](https://mlsanity.com/paper/2312.14998/) (2023)
- [Insights From A Large-Scale Database of Material Depictions In Paintings](https://mlsanity.com/paper/2011.12276/) (2020)
- [Artificial Intelligence and Misinformation in Art: Can Vision Language Models Judge the Hand or the Machine Behind the Canvas?](https://mlsanity.com/paper/2508.01408/) (2025)
- [Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS)](https://mlsanity.com/paper/2508.20322/) (2025)
- [Non-negative Elastic Net Decoding for Information Retrieval](https://mlsanity.com/paper/2606.17910/) (2026)
- [Artificial Intelligence for the Characterization of Particles and Fibers by Optical Microscopy](https://mlsanity.com/paper/2608.00361/) (2026)

## Abstract (arXiv)

Attributing an artwork to an artist has traditionally relied on detailed visual observations and descriptions, known as stylistic analysis in art history. By contrast, current artificial intelligence (AI) models used in the field offer only unexplained probabilistic classifications. To bridge this methodological gap, we present an AI framework that automates stylistic analysis of paintings, providing a foundation for enhancing evidence collection, discovery, and verification. By training a vision transformer (ViT) on a large corpus of paintings with metadata, our system encodes this art history-specific data as embeddings. These representations are factorized via sparse dictionary learning into a shared set of features that recur across the training set. A large language model (LLM) then interprets each feature by retrieving associated artworks and their accompanying curator-written texts, and synthesizes them into descriptions that reflect their stylistic attributes. Finally, an autonomous coordinator LLM applies a reasoning-and-action (ReAct) framework to weight, test, and refine these features into cohesive descriptions of an artwork, or comparisons of artworks. This approach converts detailed visual features into descriptive terms, addressing a key challenge in art history. It thus connects the use of images as data with the semantic concerns of humanists, establishing vision-based computational art history as an area for future growth.