Paper page - Editor’s Choice: Evaluating Abstract Intent in Image Editing through Atomic Entity Analysis

Source: https://huggingface.co/papers/2605.14842

Abstract

Abstract image editing benchmark and entity-rubrics framework reveal challenges in balancing intent and preservation for abstract instructions, highlighting need for advanced LLM integration and iterative approaches.

Humans naturally communicate through abstract concepts like “mood”. However, current image editingbenchmarks focus primarily on explicit, literal commands, leaving abstract instructions largely underexplored. In this work, we first formalize the definition and taxonomy ofabstract image editing. To measureinstruction-followingin this challenging domain, we introduceEntity-Rubrics, a framework that breaks down abstract edits into individual, entity-level assessments and achieves strong correlation with human judgment. Alongside this framework, we contribute AbstractEdit, the firstbenchmarkdedicated toabstract image editingacross diverse real-world scenes. Evaluating 11 leading models on this dataset reveals a fundamental challenge: standard architectures struggle to balance intent and preservation, commonly defaulting to under-editing or over-editing. Our analysis demonstrates that driving meaningful improvements relies heavily on integrating advanced LLMtext encodersanditerative thinking. Looking forward, our entity-based paradigm can generalize beyond assessment to serve as a reward model, enable models to correctly interpret abstract communication, or highlight specific failures in test-time critique loops. Ultimately, we hope this work serves as a stepping stone toward seamlessmultimodal interaction, closing the gap between rigid machine execution and the natural, open-ended way humans communicate.

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