Unified Thinker: A General Reasoning Modular Core for Image Generation
This addresses the reasoning-execution gap in image generation for users needing more logical and accurate outputs, representing a novel method rather than an incremental improvement.
The paper tackled the problem of generative models struggling with logic-intensive instruction following by proposing Unified Thinker, a task-agnostic reasoning architecture that decouples reasoning from image generation, resulting in substantial improvements in image reasoning and generation quality as shown in experiments on text-to-image generation and image editing.
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.