# Is there a benchmark for whether a generated world keeps an object or event going after the camera looks away (object permanence), and what do 23 models score?

- id: art_01M5MB51QY3QKMJ2KXPXX87NPB
- 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, T20 · tags: none
- updated: 2026-10-23T12:18:12.854Z
- url: https://veyrul.com/articles/art_01M5MB51QY3QKMJ2KXPXX87NPB

WRBench (arXiv v1, 18 Jun 2026) is the only one of the six papers whose protocol asks whether state persists when the target leaves view. It crosses 25 scene families with a four-level event design (displacement versus state change) and uses camera yaw as an intervention: a cat starts on the floor, the camera turns away and returns, and the cat should be on the bed. Six dimensions (camera precision, prompt-camera alignment, visual integrity, visible spatial and state consistency, re-observation support, re-observed consistency) are scored on 9,600 videos from 23 models. The authors report that re-observed event state stays about 0.62 to 0.66 even when the object returns, does not improve from Wan 1.3B to 14B (0.66 to 0.62), and is only judgeable in 2,073 of 9,600 videos.

WRBench (arXiv v1, 18 Jun 2026) is the only one of the six papers whose protocol asks whether state persists when the target leaves view. It crosses 25 scene families with a four-level event design (displacement versus state change) and uses camera yaw as an intervention: a cat starts on the floor, the camera turns away and returns, and the cat should be on the bed. Six dimensions (camera precision, prompt-camera alignment, visual integrity, visible spatial and state consistency, re-observation support, re-observed consistency) are scored on 9,600 videos from 23 models. The authors report that re-observed event state stays about 0.62 to 0.66 even when the object returns, does not improve from Wan 1.3B to 14B (0.66 to 0.62), and is only judgeable in 2,073 of 9,600 videos.

Curator qualifications: Curator located the excerpted evidence in the work copies (automated prefix match) and spot-checked claim-critical figures by search. The only benchmark among the six that tests off-screen persistence.

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/2606.20545v1",
      "note": "WRBench protocol"
    },
    {
      "url": "https://github.com/JinPLu/WRBench",
      "note": "WRBench protocol"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] WRBench (arXiv v1, 18 Jun 2026) is the only one of the six papers whose protocol asks whether state persists when the target leaves view. It crosses 25 scene families with a four-level event design (displacement versus state change) and uses camera yaw as an intervention: a cat starts on the floor, the camera turns away and returns, and the cat should be on the bed. Six dimensions (camera precision, prompt-camera alignment, visual integrity, visible spatial and state consistency, re-observation support, re-observed consistency) are scored on 9,600 videos from 23 models. The authors report that re-observed event state stays about 0.62 to 0.66 even when the object returns, does not improve from Wan 1.3B to 14B (0.66 to 0.62), and is only judgeable in 2,073 of 9,600 videos. · locator: WRBench protocol · source: https://arxiv.org/abs/2606.20545v1

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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