# Uncertainty-Aware Art-Historical Dating with Vision-Language Models

arXiv: https://arxiv.org/abs/2608.18984 · HTML: https://mlsanity.com/paper/2608.18984/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 19 Aug 2026.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 16 of 16)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features](https://mlsanity.com/paper/2502.14786/) (2025)
- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [EVA-CLIP: Improved Training Techniques for CLIP at Scale](https://mlsanity.com/paper/2303.15389/) (2023)
- [Conformalized Quantile Regression](https://mlsanity.com/paper/1905.03222/) (2019)
- [A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle](https://mlsanity.com/paper/1901.10002/) (2019)
- [CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification](https://mlsanity.com/paper/2204.14244/) (2022)
- [See It from My Perspective: How Language Affects Cultural Bias in Image Understanding](https://mlsanity.com/paper/2406.11665/) (2024)
- [Have Large Vision-Language Models Mastered Art History?](https://mlsanity.com/paper/2409.03521/) (2024)
- [iART: A Search Engine for Art-Historical Images to Support Research in the Humanities](https://mlsanity.com/paper/2108.01542/) (2021)
- [Blind Dates: Examining the Expression of Temporality in Historical Photographs](https://mlsanity.com/paper/2310.06633/) (2023)
- [Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach](https://mlsanity.com/paper/2106.05618/) (2021)
- [Dating Ancient Paintings of Mogao Grottoes Using Deeply Learnt Visual Codes](https://mlsanity.com/paper/1810.09168/) (2018)
- [Exploiting CLIP-based Multi-modal Approach for Artwork Classification and Retrieval](https://mlsanity.com/paper/2309.12110/) (2023)
- [Context-aware Multimodal AI Reveals Hidden Pathways in Five Centuries of Art Evolution](https://mlsanity.com/paper/2503.13531/) (2025)

## Abstract (arXiv)

Museum and archival datasets do not mirror historical artistic production, but materialize the contingent histories of collecting, preservation, cataloging, and digitization. This has direct consequences for interpreting pretrained image representations: they may appear to encode historical time while actually encoding the institutional conditions under which objects become visible as data. We describe this phenomenon as temporal entanglement and investigate it by formulating artwork dating as an uncertainty-aware regression task over frozen image embeddings. We evaluate several pretrained vision models on a temporally controlled Wikidata corpus of artworks. Our results show that these models contain usable temporal information, with Vision-Language Models (VLMs) outperforming purely visual self-supervised baselines. However, a qualitative analysis indicates that this temporal knowledge is shaped by various biases.