Update README.md

README.md

@@ -24,13 +24,13 @@ Through discussions with researchers at Safe Superintelligence (SSI) Club, Unive
 
 A similar vision was shared by [**Stefano Ermon**](https://cs.stanford.edu/~ermon/) at ICLR 2025, where he described *Diffusion as a unified paradigm for a multi-modal world model*, a message that echoes and strengthens our belief: that unified generative modeling is the path toward general-purpose superintelligence.
 
-To pursue this vision, we introduced [**Muddit** and **Muddit Plus**](https://github.com/M-E-AGI-Lab/Muddit), unified generative models built upon visual priors (Meissonic), and capable of unified generation across text and image within a single architecture and paradigm.
+To pursue this vision, we introduced [**Muddit** and **Muddit Plus**](https://github.com/M-E-AGI-Lab/Muddit), unified generative models built upon visual priors (Meissonic), and capable of generation across text and image within a single architecture and paradigm.
 
 We want to build the world with visual prior, though we sadly agree that the language prior dominates current unified models. 
 Inspired by the success of Mercury by [**Inception Labs**](https://www.inceptionlabs.ai/),
-we developed [**Lumina-DiMOO**](https://arxiv.org/abs/2510.06308). At a larger scale than Muddit, Lumina-DiMOO is a unified masked diffusion model that achieves state-of-the-art performance among discrete diffusion models to date, and we are still pushing it further! It integrates high-resolution image generation with multimodal capabilities, including text-to-image, image-to-image, and image understanding.
+we developed [**Lumina-DiMOO**](https://arxiv.org/abs/2510.06308). As a larger scale unified masked diffusion model than Muddit, Lumina-DiMOO achieves state-of-the-art performance among discrete diffusion models to date; and we are still pushing it further! It integrates high-resolution image generation with multimodal capabilities, including text-to-image, image-to-image, and image understanding.
 
-To further clarify our roadmap, we articulated our perspective in [**From Masks to Worlds: A Hitchhiker’s Guide to World Models**](https://arxiv.org/abs/2510.20668), which traces a five-stage roadmap from early masked modeling to unified generative modeling and the future we are building.
+To further clarify our long-term roadmap, we articulated our perspective in [**From Masks to Worlds: A Hitchhiker’s Guide to World Models**](https://arxiv.org/abs/2510.20668), which traces a five-stage roadmap from early masked modeling to unified generative modeling and the future we are building.
 
 We look forward to releasing more models and algorithms in this direction. We post related and family papers [here](https://github.com/viiika/Meissonic). 
 We thank our amazing teammates and you, the reader, for your interest in our work.