Unified Thinker: A General Reasoning Modular Core for Image Generation
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.
695e85d9bf9f21203a768c6f
Sashuai Zhou
695e85d9bf9f21203a768c70
Qiang Zhou
695e85d9bf9f21203a768c71
Jijin Hu
695e85d9bf9f21203a768c72
Hanqing Yang
695e85d9bf9f21203a768c73
Yue Cao
695e85d9bf9f21203a768c74
Junpeng Ma
695e85d9bf9f21203a768c75
Yinchao Ma
695e85d9bf9f21203a768c76
Jun Song
695e85d9bf9f21203a768c77
Tiezheng Ge
695e85d9bf9f21203a768c78
Cheng Yu
695e85d9bf9f21203a768c79
Bo Zheng
695e85d9bf9f21203a768c7a
Zhou Zhao
Unified Thinker: A General Reasoning Modular Core for Image Generation
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.
Unified Thinker addresses the reasoning-execution gap in image generation by decoupling a reasoning module from image generators and using reinforcement learning to optimize visual correctness.
generative models reasoning--execution gap closed-source systems executable reasoning task-agnostic reasoning architecture unified planning core image generation reinforcement learning visual correctness textual plausibility