# When driving and robot-learning papers say "world model", what exactly do they mean (inputs, outputs, intended use), and where does the word claim more than the evidence tests?

- id: art_01M5MC9P299NTTJRG5ASQQXZJY
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T06, T15, T19 · tags: none
- updated: 2026-10-23T12:38:13.316Z
- url: https://veyrul.com/articles/art_01M5MC9P299NTTJRG5ASQQXZJY

In every source inspected here a "world model" is a learned generative model that takes past observations (video) plus some control input and produces future video; the sources differ in what the model is for. GAIA-1 and Vista define it as predicting action consequences, NVIDIA as predicting the next observation given a "perturbation", 1X as an evaluator that also predicts task success. Several sources list uses (planning, policy training, evaluation) that they do not test; by 2026 Wayve and 1X frame the model as a closed-loop evaluation tool. The word is the authors' own label and none of the inspected definitions is tied to a physics-accuracy test.

In every source inspected here a "world model" is a learned generative model that takes past observations (video) plus some control input and produces future video; the sources differ in what the model is for. GAIA-1 and Vista define it as predicting action consequences, NVIDIA as predicting the next observation given a "perturbation", 1X as an evaluator that also predicts task success. Several sources list uses (planning, policy training, evaluation) that they do not test; by 2026 Wayve and 1X frame the model as a closed-loop evaluation tool. The word is the authors' own label and none of the inspected definitions is tied to a physics-accuracy test.

Curator qualifications: Sources agree a world model is a learned generator of future video from past observations plus control; they differ on purpose (action consequences, next state).

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://arxiv.org/abs/2309.17080v1",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/abs/2501.03575v3",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/abs/2405.17398v5",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/abs/2309.09777v2",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://www.1x.tech/discover/1x-world-model",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://www.1x.tech/1x-world-model.pdf",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://wayve.ai/thinking/gaia-4/",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] In every source inspected here a "world model" is a learned generative model that takes past observations (video) plus some control input and produces future video; the sources differ in what the model is for. GAIA-1 and Vista define it as predicting action consequences, NVIDIA as predicting the next observation given a "perturbation", 1X as an evaluator that also predicts task success. Several sources list uses (planning, policy training, evaluation) that they do not test; by 2026 Wayve and 1X frame the model as a closed-loop evaluation tool. The word is the authors' own label and none of the inspected definitions is tied to a physics-accuracy test. · locator: candidate evidence excerpts · source: https://arxiv.org/abs/2309.17080v1

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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