Multimodal Large Language Models have advanced visual reasoning, yet a purely textual chain of thought remains a bottleneck for questions that require fine-grained focus or view transformations. The ''think with images'' paradigm narrows this gap, but existing approaches are either constrained by fixed predefined toolkits or produce noisy intermediate images from unified multimodal methods. We pursue a third option: using a dedicated image editing model and decouple it with an understanding model. However, off-the-shelf image editors fail as reasoning assistants with two complementary gaps: a language-side gap, where editors trained as passive instruction-followers cannot map an abstract question to an appropriate visual transformation, and a generation-side gap, where edit correctness degrades as reasoning depth grows. Guided by this analysis, we introduce ETCHR (Editing To Clarify and Harness Reasoning), a question-conditioned, reasoning-aware image editor decoupled from the downstream understanding model and trained with a two-stage recipe targeted at the two gaps: Reasoning Imitation via supervised fine-tuning on edit trajectories, followed by Reasoning Enhancement with VLM-derived rewards for edit correctness and downstream reasoning accuracy. Since the editor is decoupled, ETCHR plugs into different open- and closed-source MLLMs in a training-free manner. Across five task families (fine-grained perception, chart understanding, logic reasoning, jigsaw restoration, and 3D understanding), ETCHR raises average Pass@1 from 55.95 to 60.77 (+4.82) with Qwen3-VL-8B, from 65.08 to 70.55 (+5.47) with Gemini-3.1-Flash-Lite, and from 76.55 to 81.16 (+4.61) with the 1T-parameter MoE model Kimi K2.5.

翻译：[translated abstract in Chinese] 多模态大语言模型推动了视觉推理的发展，但纯文本的思维链仍然成为需要细粒度关注或视角转换的问题的瓶颈。"用图像思考"范式缩小了这一差距，但现有方法要么受限于固定预定义工具包，要么从统一多模态方法中生成带有噪声的中间图像。我们探索第三种方案：使用专用图像编辑模型，并将其与理解模型解耦。然而，现成的图像编辑器作为推理辅助工具存在两个互补缺陷而失效：语言侧缺陷（被训练为被动指令执行者的编辑器无法将抽象问题映射到适当的视觉变换）和生成侧缺陷（随着推理深度增加，编辑正确性会下降）。基于此分析，我们提出ETCHR（通过编辑实现推理的澄清与增强）——一种与下游理解模型解耦的、问题条件化的、推理感知的图像编辑器。该编辑器采用针对这两个缺陷的两阶段训练方案：通过编辑轨迹上的监督微调进行推理模仿，随后通过基于VLM的奖励对编辑正确性和下游推理准确性进行推理增强。由于编辑器是解耦的，ETCHR能以无需训练的方式嵌入不同开源和闭源MLLM。在五个任务族（细粒度感知、图表理解、逻辑推理、拼图复原和3D理解）中，ETCHR将Qwen3-VL-8B的平均Pass@1从55.95提升至60.77（+4.82），Gemini-3.1-Flash-Lite从65.08提升至70.55（+5.47），以及1T参数MoE模型Kimi K2.5从76.55提升至81.16（+4.61）。