SMART-Editor: A Multi-Agent Framework for Human-Like Design Editing with Structural Integrity

Ishani Mondal, Meera Bharadwaj, Ayush Roy, Aparna Garimella, Jordan Lee Boyd-Graber
7/30/2025
cs.CLcs.AI

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Abstract

We present SMART-Editor, a framework for compositional layout and content editing across structured (posters, websites) and unstructured (natural images) domains. Unlike prior models that perform local edits, SMART-Editor preserves global coherence through two strategies: Reward-Refine, an inference-time rewardguided refinement method, and RewardDPO, a training-time preference optimization approach using reward-aligned layout pairs. To evaluate model performance, we introduce SMARTEdit-Bench, a benchmark covering multi-domain, cascading edit scenarios. SMART-Editor outperforms strong baselines like InstructPix2Pix and HIVE, with RewardDPO achieving up to 15% gains in structured settings and Reward-Refine showing advantages on natural images. Automatic and human evaluations confirm the value of reward-guided planning in producing semantically consistent and visually aligned edits.

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Cite this paper

@article{mondal2025smarteditor,
  title  = {SMART-Editor: A Multi-Agent Framework for Human-Like Design Editing with Structural Integrity},
  author = {Ishani Mondal and Meera Bharadwaj and Ayush Roy and Aparna Garimella and Jordan Lee Boyd-Graber},
  year   = {2025},
  eprint = {2507.23095},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.23095v2}
}

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