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-license: mit
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+---
+license: mit
+---
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+# Scaffold-and-Fill Diffusion (SF-Diff): A Hybrid Architecture for Accelerated Language Model Inference
+
+**Author:** Hilal Limo (Self-Taught Independent Researcher, Age 15)
+
+**[➡️ Click here to read the full paper: SF-Diff_Paper.pdf](SF-Diff_Paper.pdf)**
+
+---
+
+## 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.
+
+---
+
+## Citation
+
+If you find this work interesting, please consider citing the paper:
+
+```bibtex
+@misc{limo2025sfdiff,
+  author       = {Hilal Limo},
+  title        = {Scaffold-and-Fill Diffusion (SF-Diff): A Hybrid Architecture for Accelerated Language Model Inference},
+  year         = {2025},
+  publisher    = {Hugging Face},
+  journal      = {Hugging Face Hub},
+  howpublished = {\url{https://huggingface.co/TimesLast/SF-Diff}}
+}