Update README.md

README.md

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 ## Abstract
 
-Autoregressive transformer models, the dominant architecture for modern Large Language Models (LLMs), are fundamentally constrained by high inference latency due to their sequential generation process. In this paper, I propose Scaffold-and-Fill Diffusion (SF-Diff), a novel hybrid architecture designed to significantly accelerate text generation by deconstructing the task into two parallelizable stages. The core hypothesis is that natural language can be separated into a semantic "scaffolding" of keywords and a grammatical "filler" of structural words. SF-Diff first utilizes a non-autoregressive diffusion model to generate the complete semantic scaffold—a sequence of keyword vector embeddings—in a fixed number of highly parallelizable steps. Subsequently, a lightweight autoregressive transformer decoder performs a "grammatical infilling" task, weaving the structural words around the pre-generated semantic core. This approach aims to combine the holistic, parallel generation strengths of diffusion models with the grammatical precision of transformers, offering a substantial reduction in inference latency while maintaining high-quality, coherent output.
+Autoregressive transformer models, the dominant architecture for modern Large Language Models (LLMs), are fundamentally constrained by high inference latency due to their sequential generation process. In this paper, I propose Scaffold-and-Fill Diffusion (SF-Diff), a novel hybrid architecture designed to significantly accelerate text generation by deconstructing the task into two parallelizable stages. The core hypothesis is that natural language can be separated into a semantic "scaffolding" of keywords and a grammatical "filler" of structural words. SF-Diff first utilizes a non-autoregressive diffusion model to generate the complete semantic scaffold, a sequence of keyword vector embeddings—in a fixed number of highly parallelizable steps. Subsequently, a lightweight autoregressive transformer decoder performs a "grammatical infilling" task, weaving the structural words around the pre-generated semantic core. This approach aims to combine the holistic, parallel generation strengths of diffusion models with the grammatical precision of transformers, offering a substantial reduction in inference latency while maintaining high-quality, coherent output.
 
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