Agentic Design Review System
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914387254575104 |
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| author | Nag, Sayan Joseph, K J Goswami, Koustava Morariu, Vlad I Srinivasan, Balaji Vasan |
| author_facet | Nag, Sayan Joseph, K J Goswami, Koustava Morariu, Vlad I Srinivasan, Balaji Vasan |
| contents | Evaluating graphic designs involves assessing it from multiple facets like alignment, composition, aesthetics and color choices. Evaluating designs in a holistic way involves aggregating feedback from individual expert reviewers. Towards this, we propose an Agentic Design Review System (AgenticDRS), where multiple agents collaboratively analyze a design, orchestrated by a meta-agent. A novel in-context exemplar selection approach based on graph matching and a unique prompt expansion method plays central role towards making each agent design aware. Towards evaluating this framework, we propose DRS-BENCH benchmark. Thorough experimental evaluation against state-of-the-art baselines adapted to the problem setup, backed-up with critical ablation experiments brings out the efficacy of Agentic-DRS in evaluating graphic designs and generating actionable feedback. We hope that this work will attract attention to this pragmatic, yet under-explored research direction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10745 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agentic Design Review System Nag, Sayan Joseph, K J Goswami, Koustava Morariu, Vlad I Srinivasan, Balaji Vasan Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Multiagent Systems Multimedia Evaluating graphic designs involves assessing it from multiple facets like alignment, composition, aesthetics and color choices. Evaluating designs in a holistic way involves aggregating feedback from individual expert reviewers. Towards this, we propose an Agentic Design Review System (AgenticDRS), where multiple agents collaboratively analyze a design, orchestrated by a meta-agent. A novel in-context exemplar selection approach based on graph matching and a unique prompt expansion method plays central role towards making each agent design aware. Towards evaluating this framework, we propose DRS-BENCH benchmark. Thorough experimental evaluation against state-of-the-art baselines adapted to the problem setup, backed-up with critical ablation experiments brings out the efficacy of Agentic-DRS in evaluating graphic designs and generating actionable feedback. We hope that this work will attract attention to this pragmatic, yet under-explored research direction. |
| title | Agentic Design Review System |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Multiagent Systems Multimedia |
| url | https://arxiv.org/abs/2508.10745 |