To elicit capabilities for addressing complex and implicit visual requirements, recent unified multimodal models increasingly adopt chain-of-thought reasoning to guide image generation. However, the actual effect of reasoning on visual synthesis remains unclear. We present UReason, a diagnostic benchmark for reasoning-driven image generation that evaluates whether reasoning can be faithfully executed in pixels. UReason contains 2,000 instances across five task families: Code, Arithmetic, Spatial, Attribute, and Text reasoning. To isolate the role of reasoning traces, we introduce an evaluation framework comparing direct generation, reasoning-guided generation, and de-contextualized generation which conditions only on the refined prompt. Across eight open-source unified models, we observe a consistent Reasoning Paradox: Reasoning traces generally improve performance over direct generation, yet retaining intermediate thoughts as conditioning context often hinders visual synthesis, and conditioning only on the refined prompt yields substantial gains. Our analysis suggests that the bottleneck lies in contextual interference rather than insufficient reasoning capacity. UReason provides a principled testbed for studying reasoning in unified models and motivates future methods that effectively integrate reasoning for visual generation while mitigating interference.

翻译：为激发处理复杂隐式视觉需求的能力，近期统一多模态模型日益采用思维链推理来引导图像生成。然而，推理对视觉合成的实际影响尚不明确。本文提出UReason——一个用于推理驱动图像生成的诊断性评测基准，旨在评估推理能否在像素层面被忠实执行。UReason包含五个任务族（代码、算术、空间、属性与文本推理）共计2,000个实例。为分离推理轨迹的作用，我们引入一个评估框架，对比直接生成、推理引导生成以及仅以精炼提示为条件的去语境化生成。在八个开源统一模型中，我们观察到一致的推理悖论：推理轨迹通常能提升直接生成的性能，但将中间思考保留为条件上下文往往会阻碍视觉合成，而仅以精炼提示为条件则可带来显著增益。分析表明瓶颈在于上下文干扰而非推理能力不足。UReason为研究统一模型中的推理提供了原则性测试平台，并激励未来方法在有效整合推理进行视觉生成的同时缓解干扰。