# Do learned latent actions keep the same meaning across different scenes, viewpoints and bodies, and how well can a motion be transferred from one video to another?

- id: art_01M5MBWB3QDNQM0T9WMF7F0QNX
- 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, T18 · tags: none
- updated: 2026-10-23T12:30:56.112Z
- url: https://veyrul.com/articles/art_01M5MBWB3QDNQM0T9WMF7F0QNX

Evidence that latents keep meaning across scenes is mixed and mostly reported by the authors of the systems. Genie reports a CoinRun result and qualitative consistency; AdaWorld reports that its continuous latent transfers an action to new scenes (human-judged 70.5% LIBERO, 61.5% SSv2, versus 0% for an action-agnostic baseline); Olaf-World argues standard latents often fail across contexts and reports better cross-view probes after adding an alignment objective (cross-domain Macro-F1 about 0.59-0.63, versus about 0.48-0.50 for the AdaWorld baseline in its re-run). In-the-wild latents are camera-relative, so the same real action may map to different latents depending on the view.

Evidence that latents keep meaning across scenes is mixed and mostly reported by the authors of the systems. Genie reports a CoinRun result and qualitative consistency; AdaWorld reports that its continuous latent transfers an action to new scenes (human-judged 70.5% LIBERO, 61.5% SSv2, versus 0% for an action-agnostic baseline); Olaf-World argues standard latents often fail across contexts and reports better cross-view probes after adding an alignment objective (cross-domain Macro-F1 about 0.59-0.63, versus about 0.48-0.50 for the AdaWorld baseline in its re-run). In-the-wild latents are camera-relative, so the same real action may map to different latents depending on the view.

Curator qualifications: Verified the 70.5% and 61.5% transfer rates. The cross-paper comparison is correctly marked non-independent.

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/2402.15391v1",
      "note": "Table 2; human eval; limitations"
    },
    {
      "url": "https://arxiv.org/abs/2503.18938v4",
      "note": "Table 2; human eval; limitations"
    },
    {
      "url": "https://arxiv.org/abs/2602.10104v2",
      "note": "Table 2; human eval; limitations"
    },
    {
      "url": "https://arxiv.org/abs/2601.05230v2",
      "note": "Table 2; human eval; limitations"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] Evidence that latents keep meaning across scenes is mixed and mostly reported by the authors of the systems. Genie reports a CoinRun result and qualitative consistency; AdaWorld reports that its continuous latent transfers an action to new scenes (human-judged 70.5% LIBERO, 61.5% SSv2, versus 0% for an action-agnostic baseline); Olaf-World argues standard latents often fail across contexts and reports better cross-view probes after adding an alignment objective (cross-domain Macro-F1 about 0.59-0.63, versus about 0.48-0.50 for the AdaWorld baseline in its re-run). In-the-wild latents are camera-relative, so the same real action may map to different latents depending on the view. · locator: Table 2; human eval; limitations · source: https://arxiv.org/abs/2402.15391v1

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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