# How are WorldSimBench scores produced and validated, which systems lead and by how much, and which of the paper's own claims do its tables fail to support?

- id: art_01M5MC5JJ7DPSZ504GRS8PSP6Z
- 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, T20 · tags: none
- updated: 2026-10-23T12:35:58.648Z
- url: https://veyrul.com/articles/art_01M5MC5JJ7DPSZ504GRS8PSP6Z

WorldSimBench validates its learned evaluator against GPT-4o with PLCC (0.60 AD, 0.43 RM in-distribution; zero-shot 0.34-0.49 AD, 0.44-0.47 RM) and accuracy for OE, but gives no evaluation-set sizes, annotator counts or agreement. The explicit and implicit modes disagree: by my computation OE rank agreement across seven models is Spearman 0.04 (0.21 on the five text-conditioned models). The authors admit inflated scores in robot manipulation and Velocity, and several statements in the text do not match the paper's own tables (GPT-4o negative-correlation attribution, 'AD variation is minimal', Open-Sora-Plan's trajectory lead). All results are for eight older open models fine-tuned on scenario data, so 'who leads' says nothing about current commercial systems.

WorldSimBench validates its learned evaluator against GPT-4o with PLCC (0.60 AD, 0.43 RM in-distribution; zero-shot 0.34-0.49 AD, 0.44-0.47 RM) and accuracy for OE, but gives no evaluation-set sizes, annotator counts or agreement. The explicit and implicit modes disagree: by my computation OE rank agreement across seven models is Spearman 0.04 (0.21 on the five text-conditioned models). The authors admit inflated scores in robot manipulation and Velocity, and several statements in the text do not match the paper's own tables (GPT-4o negative-correlation attribution, 'AD variation is minimal', Open-Sora-Plan's trajectory lead). All results are for eight older open models fine-tuned on scenario data, so 'who leads' says nothing about current commercial systems.

Curator qualifications: Table 3 in the work copy shows AD PLCC GPT-4o@OpenSora 0.03 and GPT-4o@Lavie -0.04, RM GPT-4o@OpenSora -0.06 and GPT-4o@Lavie 0.17, while Section 5.2 says GPT-4o is negative for OpenSora in AD and Lavie in RM: names appear swapped, as the producer says. PLCC values 0.60/0.43 and zero-shot ranges located. No evaluation-set sizes found.

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/html/2410.18072v1",
      "note": "Table 3 and Section 5.2"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] WorldSimBench validates its learned evaluator against GPT-4o with PLCC (0.60 AD, 0.43 RM in-distribution; zero-shot 0.34-0.49 AD, 0.44-0.47 RM) and accuracy for OE, but gives no evaluation-set sizes, annotator counts or agreement. The explicit and implicit modes disagree: by my computation OE rank agreement across seven models is Spearman 0.04 (0.21 on the five text-conditioned models). The authors admit inflated scores in robot manipulation and Velocity, and several statements in the text do not match the paper's own tables (GPT-4o negative-correlation attribution, 'AD variation is minimal', Open-Sora-Plan's trajectory lead). All results are for eight older open models fine-tuned on scenario data, so 'who leads' says nothing about current commercial systems. · locator: Table 3 and Section 5.2 · source: https://arxiv.org/html/2410.18072v1

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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