mlsanityData to 14 Oct 2026

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

arXiv 2608.18984 ↗First posted 19 Aug 2026cs.CVcited by 0

On the map: Multimodal LLMs / vision-language models → VLM benchmarks and visual reasoning

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.

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.

Builds on

16 of 36 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Have Large Vision-Language Models Mastered Art History?5 Sep
  2. 2024See It from My Perspective: How Language Affects Cultural Bias in Image Understanding17 Jun

Other references · 14

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  2. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  3. 2025SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Featurescited by 1,188
  4. 2023Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecturecited by 1,039
  5. 2023EVA-CLIP: Improved Training Techniques for CLIP at Scalecited by 798
  6. 2019Conformalized Quantile Regressioncited by 629
  7. 2019A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cyclecited by 193
  8. 2022CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classificationcited by 65
  9. 2021iART: A Search Engine for Art-Historical Images to Support Research in the Humanitiescited by 6
  10. 2023Blind Dates: Examining the Expression of Temporality in Historical Photographscited by 5
  11. 2021Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approachcited by 4
  12. 2018Dating Ancient Paintings of Mogao Grottoes Using Deeply Learnt Visual Codescited by 3
2 more
  1. 2023Exploiting CLIP-based Multi-modal Approach for Artwork Classification and Retrievalcited by 2
  2. 2025Context-aware Multimodal AI Reveals Hidden Pathways in Five Centuries of Art Evolutioncited by 2

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  1. 2026On the Cultural Anachronism and Temporal Reasoning in Vision Language Models
  2. 2024Understanding Museum Exhibits using Vision-Language Reasoning
  3. 2026On the Explainability of Vision-Language Models in Art History
  4. 2026MMArt: A Multi-Perspective Multimodal Dataset for Visual Art Understanding
  5. 2026Art Beyond Semantics: Sheaf-Informed Contrastive Learning for Multi-Relational Representations
  6. 2025Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts
  7. 2026Does AI See like Art Historians? Interpreting How Vision Language Models Recognize Artistic Style
  8. 2023Knowledge-Aware Artifact Image Synthesis with LLM-Enhanced Prompting and Multi-Source Supervision