---
schema: 1
type: term
title: '**autoregressive**'
description: >-
  **autoregressive** — describing a model that generates output step by step,
  where each step (such as the next token or next video frame) is predicted from
  everything generated so far. In this channel's material the term characterizes
  how **LLMs** generate text, and it also appears in **hybrid
  diffusion–autoregressive** video architectures.
canonical: >-
  https://liquidgold.cc/channel/everyinc/explore/term/autoregressive-aae0f3e827d5
markdown: >-
  https://liquidgold.cc/channel/everyinc/explore/term/autoregressive-aae0f3e827d5.md
llms: null
parent: 'https://liquidgold.cc/channel/everyinc/llms.txt'
channel: everyinc
updated: '2026-10-23T22:12:35.346Z'
language: null
children: []
---


# \*\*autoregressive\*\*

Canonical: https://liquidgold.cc/channel/everyinc/explore/term/autoregressive-aae0f3e827d5

Parent: [Channel index](https://liquidgold.cc/channel/everyinc/llms.txt)

## Definition

**autoregressive** — describing a model that generates output step by step, where each step (such as the next token or next video frame) is predicted from everything generated so far. In this channel's material the term characterizes how **LLMs** generate text, and it also appears in **hybrid diffusion–autoregressive** video architectures.

In this channel's material, **autoregressive** appears in two connected places. First, it names the generation style of LLMs: predicting the next token from the tokens already produced. That one-token-at-a-time loop is what makes LLMs, in the captured material, *architecturally awkward for mission-critical work* — the model cannot easily enforce verifiable, inspectable correctness in the way the episode contrasts with **energy-based models**.[^handout] Second, it shows up in the phrase **hybrid diffusion–autoregressive video models**: architectures that combine diffusion-style generation with autoregressive token-by-token continuation to reach *real-time video* and address problems that LLMs alone cannot, such as spatial reasoning and exact determinism.[^topic-summary]The episode treats *autoregressive* as a label for a family of architectures with a shared constraint — output is built sequentially, each step conditioned on what came before. Understanding the label helps you see the throughline of the chapter: identifying which problems fall **outside the limits of LLMs** (verifiable correctness, spatial reasoning, exact determinism) and which newer architectures — energy-based models, hybrid diffusion–autoregressive video models, asynchronously orchestrated coding agents — are built to solve them.[^topic]\- **Autoregressive** = generating output one token or frame at a time, each step conditioned on prior steps.\\- It characterizes **LLMs**, which the source frames as awkward for mission-critical, verifiable work.\\- It also appears in **hybrid diffusion–autoregressive video models**, a newer architecture for real-time video.\\- Knowing the term helps you understand which problems the chapter says fall outside LLM limits.\