@inproceedings{mondal-etal-2026-smart,
    title = "{SMART}-Editor: A Multi-Agent Framework for Human-Like Design Editing with Structural Integrity",
    author = "Mondal, Ishani  and
      Bharadwaj, Meera  and
      Roy, Ayush  and
      Garimella, Aparna  and
      Boyd-Graber, Jordan Lee",
    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://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.169/",
    pages = "3219--3245",
    ISBN = "979-8-89176-386-9",
    abstract = "Despite significant progress in natural image editing with state-of-the-art MLLMs, compositional layout and content editing for structured visual domains (e.g., posters, websites) remains underexplored. In this work, we introduce SMART-EDITOR, a multi-agent framework for compositional editing for structured images like posters or websites. Unlike prior models that focus on isolated local edits, SMART-EDITOR maintains global coherence through two complementary strategies: Reward-Refine, an inference-time reward-guided refinement method, and RewardDPO, a training-time preference optimization approach leveraging reward-aligned layout pairs. To evaluate performance, we introduce SMARTEdit-Bench, a benchmark of cascading multi-step edit instructions that are implicit in nature yet require layout and semantic-consistency preserving reasoning about edit order to preserve spatial and semantic consistency. Both automatic and human evaluations confirm the value of reward-guided planning in producing semantically consistent and visually coherent edits, beyond what single-shot VLMs can generate."
}