统一思考者：一种通用图像生成的推理模块核心
Unified Thinker: A General Reasoning Modular Core for Image Generation
AI总结：
本文提出统一思考者，一种通用图像生成的推理模块核心，旨在通过可执行推理解决生成模型中的推理-执行差距，提升图像生成质量。
摘要：
尽管高保真图像合成取得了显著进展，生成模型在处理逻辑密集型指令跟随时仍存在困难，暴露出持续的推理-执行差距。同时，闭源系统（如Nano Banana）展示了强大的推理驱动图像生成能力，凸显了与当前开源模型之间的显著差距。我们主张，解决这一差距需要的不仅仅是更好的视觉生成器，而是可执行推理：将高层次意图分解为可验证的计划，直接引导生成过程。为此，我们提出了统一思考者，一种任务无关的推理架构，用于通用图像生成，设计为一个统一的规划核心，可插件到多样的生成器和工作流程中。统一思考者将专用的Thinker与图像生成器解耦，使推理模块的升级无需重新训练整个生成模型。我们进一步引入了双阶段训练范式：首先构建一个结构化的规划接口用于Thinker，然后应用强化学习使其策略基于像素级反馈进行校准，鼓励优化视觉正确性而非文本合理性。在文本到图像生成和图像编辑的广泛实验中，统一思考者显著提升了图像推理和生成质量。
英文摘要：
Despite impressive progress in high-fidelity image synthesis, generative models still struggle with logic-intensive instruction following, exposing a persistent reasoning--execution gap. Meanwhile, closed-source systems (e.g., Nano Banana) have demonstrated strong reasoning-driven image generation, highlighting a substantial gap to current open-source models. We argue that closing this gap requires not merely better visual generators, but executable reasoning: decomposing high-level intents into grounded, verifiable plans that directly steer the generative process. To this end, we propose Unified Thinker, a task-agnostic reasoning architecture for general image generation, designed as a unified planning core that can plug into diverse generators and workflows. Unified Thinker decouples a dedicated Thinker from the image Generator, enabling modular upgrades of reasoning without retraining the entire generative model. We further introduce a two-stage training paradigm: we first build a structured planning interface for the Thinker, then apply reinforcement learning to ground its policy in pixel-level feedback, encouraging plans that optimize visual correctness over textual plausibility. Extensive experiments on text-to-image generation and image editing show that Unified Thinker substantially improves image reasoning and generation quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
