Standard 4 stops to get here · leads to 3
Autoregressive Model
A model that generates output one token at a time, using previously generated tokens as input for the next prediction.
Your route here
4 stops · basics first
- Natural Language Processing ✓ understood
The field of AI that lets computers read, interpret, translate and generate human language, from spam filters and search to chatbots.
- Token ✓ understood
The basic unit of text that a language model processes, typically representing a word, subword, or character. Tokens are the fundamental building blocks for LLM input and output.
- Tokenization ✓ understood
Splitting text into tokens, usually subword pieces, and mapping each to an integer ID so a language model can process it.
- Language Modeling ✓ understood
Learning probability distributions over sequences of words to predict what comes next.
- Autoregressive Model · you are here ✓ understood
Where it sits
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Language Modeling Autoregressive Model
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Language & LLMs GPT Generative Pre-trained Transformer - an autoregressive language model architecture that predicts the next token given previous context. Language & LLMs Causal Language Modeling Training a model to predict the next token given previous tokens, the foundation of autoregressive models like GPT. Language & LLMs Decoder-Only Model A transformer architecture with only decoder layers, using causal masking for autoregressive generation (GPT family). Language & LLMs Text Generation Automatically creating coherent text using language models, from simple completion to creative writing. Language & LLMs Causal Mask An attention mask ensuring tokens can only attend to previous positions, crucial for autoregressive generation.
In the research
All papers →6 papers that build on Autoregressive Model ; showing 5, canon first.
Frontier · Sep 2025 · 189 citations LongLive: Real-time Interactive Long Video Generation Minute-long, steerable video at 20 frames per second on one GPU makes interactive video generation real. 
Frontier · Oct 2025 · 156 citations Self-Forcing++: Towards Minute-Scale High-Quality Video Generation It shows a way to long video without long training videos or long-video teachers. 
Frontier · Dec 2025 · 123 citations LLaDA2.0: Scaling Up Diffusion Language Models to 100B It shows diffusion LLMs can reach frontier scale by inheriting from existing models. 
Frontier · Sep 2025 · 114 citations Fast-dLLM v2: Efficient Block-Diffusion LLM A cheap path to faster inference from models you already have. 
Frontier · Oct 2025 · 109 citations Emu3.5: Native Multimodal Models are World Learners A strong case for one next-token model as a world learner across vision and language.