這個編輯是否正確？一個用於推理感知圖像編輯的多維度基準

摘要

基於擴散的圖像編輯技術在自然語言指令下已實現高度視覺真實性，然而現有系統大多仍停留在表面指令遵循層次，缺乏對使用者真實請求中隱含脈絡約束的推理能力，導致生成在視覺上合理但邏輯不一致的編輯結果。本研究提出RE-Edit，一個專為推理感知式圖像編輯設計的基準測試，從物理、環境、文化、因果與指涉五個互補推理維度評估圖像編輯系統。RE-Edit包含1,000組精心篩選的樣本，每組樣本設計成僅靠視覺合理性不足，正確編輯必須滿足隱含的邏輯約束。為支援細粒度分析，我們建立維度對齊的評估標準，並對十個開源與兩個商用圖像編輯模型進行全面研究。結果顯示，即使先進系統在產出高品質視覺結果的同時，仍經常在隱含的多維推理上遭遇困難。我們進一步提出一個輕量級的推理引導後編輯基線方法作為初步探索，說明在模型無關的架構下插入顯式推理如何協助緩解此類失誤。

English

Diffusion-based image editing has achieved strong visual fidelity under natural language instructions, yet most existing systems still operate at the level of surface instruction following, without reasoning about the implicit contextual constraints embedded in real user requests. This often leads to visually plausible but logically inconsistent edits. In this work, we introduce RE-Edit, a benchmark for REasoning-aware image Editing that evaluates image editing systems across five complementary reasoning dimensions: physical, environmental, cultural, causal, and referential. RE-Edit comprises 1,000 carefully curated samples, each designed such that visual plausibility alone is insufficient and correct editing requires satisfying implicit logical constraints. To support fine-grained analysis, we establish dimension-aligned evaluation criteria and conduct a comprehensive study of ten open-source and two commercial image editing models. Our results show that even advanced systems frequently struggle with implicit multi-dimensional reasoning despite producing high-quality visuals. We further present a lightweight reasoning-guided post-edit baseline as an initial exploration, illustrating how inserting explicit reasoning can help mitigate such failures in a model-agnostic manner.