UniReflect: Self-Reflection Tuning for Unified Multimodal Understanding and Generation

Recent studies indicate that test-time scaling (TTS) or re-flection mechanisms can substantially improve performance in multi-modal generative tasks. At the core of TTS lies verification, which as-sesses the semantic alignment between textual instructions and generatedimages. Such reflection inherently couples generation and understanding,its integration into a single unified model for visual comprehension andsynthesis remains largely unexplored. Existing methods typically dependon external Visual Large Language Models (VLLMs) for verification, re-sulting in additional computational overhead and fragmented pipelines.We introduce UniReflect, the first unified framework for visual un-derstanding and generation that performs in-model verificationvia self-reflection tuning. The proposed model is explicitly trained to as-sess the correspondence between the prompt and the generated image,produce a structured analysis, and output a special token that guides thedecoding of a refined image, which enables seamless TTS within a singleunified model, eliminating the need for external verifiers or extra edit-ing prompts. Extensive experiments demonstrate that UniReflect notonly advances image generation fidelity but also provides reliable visualverification, setting a new benchmark for unified multi-modal models.