Unlocking Complex Image Editing via Natively Interleaved Visual Textual CoT with Deep Confidence Reasoning

Abstract

Language guided image editing has gained significant pop-ularity, yet existing methods struggle with intricate object intersectionsand fine grained spatial relationships due to the absence of an explicitvisual reasoning process. While Chain of Thought (CoT) has been ex-plored to enhance reasoning, purely textual CoT or coordinate basedprompts are fundamentally limited in representing intricate visual lay-outs and lack the pixel level cues necessary for precise editing. To addressthese challenges, we propose Unlocking Complex Image Editing viaMultimodal Reasoning Edit (MURE), a natively multimodal frame-work that shifts the editing process from purely verbal reasoning to asequence of native interleaved textual and visual rationales. As aunified monolithic model, MURE enables seamless end to end optimiza-tion by sharing a latent space and reusing KV cache, which provides amore flexible intermediate visual process and mitigates the error prop-agation typical of multi tool systems. To further ensure reasoning reli-ability, we introduce the Multimodal Deep Confidence (MMDC)paradigm. This paradigm explores a tree of visual reasoning paths ateach step, pruning low quality branches via a deep confidence score froma reward model to ensure a high fidelity trajectory toward the final re-sult. We define the formal representation for native interleaved chainsand release CoT-Edit 14K, the first dataset comprising 14K high qual-ity multimodal reasoning examples. Extensive experiments demonstratethat MURE establishes a new state of the art in complex editing, achiev-ing an average gain of 7.5% on the MagicBrush benchmark and show-casing superior spatial reasoning capabilitiesThe code and CoT-Edit-14Kdataset are released at https://github.com/zhentao-zou/MURE.