@inproceedings{ge-etal-2026-vasevqa,
title = "{V}ase{VQA}: Multimodal Agent and Benchmark for {A}ncient {G}reek Pottery",
author = "Ge, Jinchao and
Cheng, Tengfei and
Wu, Biao and
Zhang, Zeyu and
Huang, Shiya and
Bishop, Judith and
Shepherd, Gillian and
Fang, Meng and
Chen, Ling and
Zhao, Yang",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-eacl.60/",
pages = "1154--1167",
ISBN = "979-8-89176-386-9",
abstract = "Understanding cultural heritage artifacts such as ancient Greek pottery requires expert-level reasoning that remains challenging for current MLLMs due to limited domain-specific data. We introduce VaseVQA, a benchmark for ancient Greek pottery, primarily vases, consisting of 31,773 images and 67,614 question{--}answer pairs across seven expert-defined categories, enabling systematic evaluation of expert-level cultural heritage understanding. Using this dataset, we explore effective training strategies for domain-specific reasoning. While supervised fine-tuning improves adaptation to domain knowledge, it struggles with deeper reasoning tasks. We propose VaseVL, which augments SFT with reinforcement learning using verifiable rewards. Experiments show that VaseVL consistently outperforms supervised baselines, especially on reasoning-intensive questions, highlighting the value of targeted reinforcement learning for cultural heritage visual question answering."
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<abstract>Understanding cultural heritage artifacts such as ancient Greek pottery requires expert-level reasoning that remains challenging for current MLLMs due to limited domain-specific data. We introduce VaseVQA, a benchmark for ancient Greek pottery, primarily vases, consisting of 31,773 images and 67,614 question–answer pairs across seven expert-defined categories, enabling systematic evaluation of expert-level cultural heritage understanding. Using this dataset, we explore effective training strategies for domain-specific reasoning. While supervised fine-tuning improves adaptation to domain knowledge, it struggles with deeper reasoning tasks. We propose VaseVL, which augments SFT with reinforcement learning using verifiable rewards. Experiments show that VaseVL consistently outperforms supervised baselines, especially on reasoning-intensive questions, highlighting the value of targeted reinforcement learning for cultural heritage visual question answering.</abstract>
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%0 Conference Proceedings
%T VaseVQA: Multimodal Agent and Benchmark for Ancient Greek Pottery
%A Ge, Jinchao
%A Cheng, Tengfei
%A Wu, Biao
%A Zhang, Zeyu
%A Huang, Shiya
%A Bishop, Judith
%A Shepherd, Gillian
%A Fang, Meng
%A Chen, Ling
%A Zhao, Yang
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F ge-etal-2026-vasevqa
%X Understanding cultural heritage artifacts such as ancient Greek pottery requires expert-level reasoning that remains challenging for current MLLMs due to limited domain-specific data. We introduce VaseVQA, a benchmark for ancient Greek pottery, primarily vases, consisting of 31,773 images and 67,614 question–answer pairs across seven expert-defined categories, enabling systematic evaluation of expert-level cultural heritage understanding. Using this dataset, we explore effective training strategies for domain-specific reasoning. While supervised fine-tuning improves adaptation to domain knowledge, it struggles with deeper reasoning tasks. We propose VaseVL, which augments SFT with reinforcement learning using verifiable rewards. Experiments show that VaseVL consistently outperforms supervised baselines, especially on reasoning-intensive questions, highlighting the value of targeted reinforcement learning for cultural heritage visual question answering.
%U https://aclanthology.org/2026.findings-eacl.60/
%P 1154-1167
Markdown (Informal)
[VaseVQA: Multimodal Agent and Benchmark for Ancient Greek Pottery](https://aclanthology.org/2026.findings-eacl.60/) (Ge et al., Findings 2026)
ACL
- Jinchao Ge, Tengfei Cheng, Biao Wu, Zeyu Zhang, Shiya Huang, Judith Bishop, Gillian Shepherd, Meng Fang, Ling Chen, and Yang Zhao. 2026. VaseVQA: Multimodal Agent and Benchmark for Ancient Greek Pottery. In Findings of the Association for Computational Linguistics: EACL 2026, pages 1154–1167, Rabat, Morocco. Association for Computational Linguistics.