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ARXIV:2604.19689 · MULTIMODAL AI · SUBMITTED 22 APR · 20:32 UTC · FRESHNESS STALE
ARXIV:2604.19689MULTIMODAL AISUBMITTED 22 APR · 20:32 UTCFRESHNESS STALEShuai Wang · Hongyi Zhu · Jia-Hong Huang · Yixian Shen · Chengxi Zeng · Stevan Rudinac · +3 at arXiv
A-MAR is an agent-based multimodal retrieval framework for fine-grained artwork understanding, enabling interpretable and grounded explanations.
Opportunity summary
Pain A-MAR is an agent-based multimodal retrieval framework for fine-grained artwork understanding, enabling interpretable and grounded explanations.
Evidence 0 refs | 4 sources | 83% coverage
Blocker Evidence partial
A-MAR is an agent-based multimodal retrieval framework for fine-grained artwork understanding, enabling interpretable and grounded explanations. While recent multimodal large language models show promise in artwork explanation, they rely on implicit reasoning and internalized…
Understanding artworks requires multi-step reasoning over visual content and cultural, historical, and stylistic context. While recent multimodal large language models show promise in artwork explanation, they rely on implicit reasoning and internalized knowl- edge,…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. While recent multimodal large language models show promise in artwork explanation, they rely on implicit reasoning and internalized knowl- edge, limiting interpretability and explicit…
Multimodal AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A-MAR is an agent-based multimodal retrieval framework for fine-grained artwork understanding, enabling interpretable and grounded explanations.
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10.48550/arXiv.2604.19689A-MAR is an agent-based multimodal retrieval framework for fine-grained artwork understanding, enabling interpretable and grounded explanations.
Abstract
Understanding artworks requires multi-step reasoning over visual content and cultural, historical, and stylistic context. While recent multimodal large language models show promise in artwork explanation, they rely on implicit reasoning and internalized knowl- edge, limiting interpretability and explicit evidence grounding. We propose A-MAR, an Agent-based Multimodal Art Retrieval framework that explicitly conditions retrieval on structured reasoning plans. Given an artwork and a user query, A-MAR first decomposes the task into a structured reasoning plan that specifies the goals and evidence requirements for each step. Retrieval is then conditionedon this plan, enabling targeted evidence selection and supporting step-wise, grounded explanations. To evaluate agent-based multi- modal reasoning within the art domain, we introduce ArtCoT-QA. This diagnostic benchmark features multi-step reasoning chains for diverse art-related queries, enabling a granular analysis that extends beyond simple final answer accuracy. Experiments on SemArt and Artpedia show that A-MAR consistently outperforms static, non planned retrieval and strong MLLM baselines in final explanation quality, while evaluations on ArtCoT-QA further demonstrate its advantages in evidence grounding and multi-step reasoning ability. These results highlight the importance of reasoning-conditioned retrieval for knowledge-intensive multimodal understanding and position A-MAR as a step toward interpretable, goal-driven AI systems, with particular relevance to cultural industries. The code and data are available at: https://github.com/ShuaiWang97/A-MAR.
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PROBLEM
A-MAR is an agent-based multimodal retrieval framework for fine-grained artwork understanding, enabling interpretable and grounded explanations. While recent multimodal large language models show promise in artwork explanation, they rely on implicit reasoning and internalized kn...
METHOD
Understanding artworks requires multi-step reasoning over visual content and cultural, historical, and stylistic context. While recent multimodal large language models show promise in artwork explanation, they rely on implicit reasoning and internalized knowl- edge, limiting int...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. While recent multimodal large language models show promise in artwork explanation, they rely on implicit reasoning and internalized knowl- edge, limiting interpretability and explicit evidence grounding....
WHY NOW
Multimodal AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
{"file name": "input.pdf", "number of pages": 10, "author": "Shuai Wang; Hongyi Zhu; Jia-Hong Huang; Yixian Shen; Chengxi Zeng; Stevan Rudinac; Monika Kackovic; Nachoem Wijnberg; Marcel Worring"
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A-MAR is an agent-based multimodal retrieval framework for fine-grained artwork understanding, enabling interpretable and grounded explanations.
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