# What does WorldMemBench's fixed-reference multi-revisit protocol measure, what did it find about decay across returns, axis switches and off-screen events, and how reliable are its checks?

- id: art_01M5MCMX337MQ9385EFEFRX1T3
- 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: 2026-10-23T12:44:20.958Z
- url: https://veyrul.com/articles/art_01M5MCMX337MQ9385EFEFRX1T3

WorldMemBench (260 cases: 200 static with human, object and environment targets, 60 dynamic with off-screen events) compares every prescribed return with the same first-visit reference over translation loops and yaw/pitch cycles, giving a score per revisit and counting a missing target as zero. On nine open video world models, the static leader (HY-WorldPlay) initiates only 31.7% of prescribed events, scores decay from the first to the third return on all eight metrics, and switching from a yaw cycle to a pitch cycle costs more. The paper adds pose-qualified subsets to separate memory from bad camera execution, and a context-policy study on LingBot World v2. Human agreement is 78.5-92.5% for static metrics but model-level correlation varies from 0.301 to 0.988.

WorldMemBench (260 cases: 200 static with human, object and environment targets, 60 dynamic with off-screen events) compares every prescribed return with the same first-visit reference over translation loops and yaw/pitch cycles, giving a score per revisit and counting a missing target as zero. On nine open video world models, the static leader (HY-WorldPlay) initiates only 31.7% of prescribed events, scores decay from the first to the third return on all eight metrics, and switching from a yaw cycle to a pitch cycle costs more. The paper adds pose-qualified subsets to separate memory from bad camera execution, and a context-policy study on LingBot World v2. Human agreement is 78.5-92.5% for static metrics but model-level correlation varies from 0.301 to 0.988.

Curator qualifications: 14 of 14 paper excerpts present (two table rows confirmed in the layout extraction: Table 2 'Overall ↑ 0.294 0.046 0.240 0.415 0.775 0.808 0.696 0.706 0.563'; Table 12 'Object Appearance 155 9 0.229 0.158 9/9 Tracking'). 260 cases, 200 static and 60 dynamic, 23 categories; HY-WorldPlay 'initiates only 31.7% of prescribed events'; agreement 78.5%-92.5%; Spearman 0.301 (Environment Depth) to 0.988 (Environment Visible); dynamic judge Spearman 0.732; '50 of 63 model–metric pairs degrade more under LRUD'; latency 322.16/262.69/260.42 s. Source variance (not a candidate error): main text says Human Identity drops 0.217 from R1 to R3 as a nine-model average, while Table 12 'All cases' gives 0.237 for Human Identity (N 125, M 8 models), i.e. different aggregation; Human Appearance is positive for 5/7 models in Table 12 although the main text says every model is weaker at R3 on the Human metrics (subset/aggregation difference not resolved). arXiv v1 dated 13 Oct 2026. Rights/verification notes are shown as limitations (L00 0019/ADR-0011): they restrict reuse of the cited material or record what was not verified; they are not cleared by this summary.

Limitations: [rights_conflict_record] Datasets/media rights conflict as in W11-05.

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/2610.19063v1",
      "note": "arXiv 2610.19063v1 PDF (pdftotext and -layout): Sec. 1, 3.3, 4.1, 4.5, 5.2-5.5, Table 2 line 554, Table 12 lines 1683-1689, App. C, I; README License and Quick Start"
    },
    {
      "url": "https://github.com/ZJU-REAL/WorldMemBench",
      "note": "arXiv 2610.19063v1 PDF (pdftotext and -layout): Sec. 1, 3.3, 4.1, 4.5, 5.2-5.5, Table 2 line 554, Table 12 lines 1683-1689, App. C, I; README License and Quick Start"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] WorldMemBench (260 cases: 200 static with human, object and environment targets, 60 dynamic with off-screen events) compares every prescribed return with the same first-visit reference over translation loops and yaw/pitch cycles, giving a score per revisit and counting a missing target as zero. On nine open video world models, the static leader (HY-WorldPlay) initiates only 31.7% of prescribed events, scores decay from the first to the third return on all eight metrics, and switching from a yaw cycle to a pitch cycle costs more. The paper adds pose-qualified subsets to separate memory from bad camera execution, and a context-policy study on LingBot World v2. Human agreement is 78.5-92.5% for static metrics but model-level correlation varies from 0.301 to 0.988. · locator: arXiv 2610.19063v1 PDF (pdftotext and -layout): Sec. 1, 3.3, 4.1, 4.5, 5.2-5.5, Table 2 line 554, Table 12 lines 1683-1689, App. C, I; README License and Quick Start · source: https://arxiv.org/abs/2610.19063v1

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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