# What exactly does R2M-Bench compute for revisit memory (pair mining, MemoryGain, NMR), how well does it track humans and resist the slow-motion shortcut, what did it find, and what can be reused?

- id: art_01M5MC0Q12S2DSGV79420FKP8Y
- 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: 2026-10-23T12:33:19.388Z
- url: https://veyrul.com/articles/art_01M5MC0Q12S2DSGV79420FKP8Y

R2M-Bench defines revisit memory as behaviour: after a commanded return the output should recover the earlier scene more than comparable non-revisit moments in the same video. For each revisit pair it samples a gap-matched non-revisit pair and a short-range pair from the same rollout, reports MemoryGain (revisit minus baseline) and a Normalized Memory Ratio (gain divided by short-range minus baseline) over five families (appearance, scene identity, object identity, local geometry, persistent state). On 100 scenes x 3 trajectories, DreamX-World-Memo leads (Overall NMR 0.706); raw similarity tracks humans better (0.594) than Overall NMR (0.547) but is coupled to low motion. The benchmark team and the top model share an organisation. Code and 100 reference images are MIT-labelled; you generate the videos.

R2M-Bench defines revisit memory as behaviour: after a commanded return the output should recover the earlier scene more than comparable non-revisit moments in the same video. For each revisit pair it samples a gap-matched non-revisit pair and a short-range pair from the same rollout, reports MemoryGain (revisit minus baseline) and a Normalized Memory Ratio (gain divided by short-range minus baseline) over five families (appearance, scene identity, object identity, local geometry, persistent state). On 100 scenes x 3 trajectories, DreamX-World-Memo leads (Overall NMR 0.706); raw similarity tracks humans better (0.594) than Overall NMR (0.547) but is coupled to low motion. The benchmark team and the top model share an organisation. Code and 100 reference images are MIT-labelled; you generate the videos.

Curator qualifications: Excerpted evidence located (one excerpt-less row); raw similarity correlates better with humans (0.594) than relative NMR (0.547) - authors' trade-off kept.

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/2608.27328v1",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://github.com/AMAP-ML/R2MBench",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] R2M-Bench defines revisit memory as behaviour: after a commanded return the output should recover the earlier scene more than comparable non-revisit moments in the same video. For each revisit pair it samples a gap-matched non-revisit pair and a short-range pair from the same rollout, reports MemoryGain (revisit minus baseline) and a Normalized Memory Ratio (gain divided by short-range minus baseline) over five families (appearance, scene identity, object identity, local geometry, persistent state). On 100 scenes x 3 trajectories, DreamX-World-Memo leads (Overall NMR 0.706); raw similarity tracks humans better (0.594) than Overall NMR (0.547) but is coupled to low motion. The benchmark team and the top model share an organisation. Code and 100 reference images are MIT-labelled; you generate the videos. · locator: candidate evidence excerpts · source: https://arxiv.org/abs/2608.27328v1

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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