# What does EntityBench report about keeping characters, objects and locations consistent across up to 50 shots, under which metrics and settings, and which systems were compared?

- id: art_01M5MBYAQ7JME9SB833W8696GN
- 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, T20 · tags: none
- updated: 2026-10-23T12:32:01.248Z
- url: https://veyrul.com/articles/art_01M5MBYAQ7JME9SB833W8696GN

EntityBench (arXiv 2605.15199v1, 14 May 2026, ByteDance/Rice) is a benchmark of 140 episodes and 2,491 shots with per-shot schedules for characters, objects and locations, scored on 51 metrics in three pillars. Four systems were compared: HoloCine, CineTrans, StoryMem and the authors' EntityMem. The authors report that cross-shot consistency drops with the number of shots between appearances, that DINOv2 face similarity and LLM identity accuracy rank the systems differently, and that EntityMem (their own per-entity reference memory on top of StoryMem, no extra training) has the highest LLM character-identity scores (llm_face_accuracy 0.406 vs 0.226 StoryMem, 0.228 HoloCine, 0.091 CineTrans). Everything is authors-run; no code or data link was found in the paper bytes.

EntityBench (arXiv 2605.15199v1, 14 May 2026, ByteDance/Rice) is a benchmark of 140 episodes and 2,491 shots with per-shot schedules for characters, objects and locations, scored on 51 metrics in three pillars. Four systems were compared: HoloCine, CineTrans, StoryMem and the authors' EntityMem. The authors report that cross-shot consistency drops with the number of shots between appearances, that DINOv2 face similarity and LLM identity accuracy rank the systems differently, and that EntityMem (their own per-entity reference memory on top of StoryMem, no extra training) has the highest LLM character-identity scores (llm_face_accuracy 0.406 vs 0.226 StoryMem, 0.228 HoloCine, 0.091 CineTrans). Everything is authors-run; no code or data link was found in the paper bytes.

Curator qualifications: All excerpts located; DINOv2 vs LLM ranking disagreement (StoryMem vs EntityMem) 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/html/2605.15199v1",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] EntityBench (arXiv 2605.15199v1, 14 May 2026, ByteDance/Rice) is a benchmark of 140 episodes and 2,491 shots with per-shot schedules for characters, objects and locations, scored on 51 metrics in three pillars. Four systems were compared: HoloCine, CineTrans, StoryMem and the authors' EntityMem. The authors report that cross-shot consistency drops with the number of shots between appearances, that DINOv2 face similarity and LLM identity accuracy rank the systems differently, and that EntityMem (their own per-entity reference memory on top of StoryMem, no extra training) has the highest LLM character-identity scores (llm_face_accuracy 0.406 vs 0.226 StoryMem, 0.228 HoloCine, 0.091 CineTrans). Everything is authors-run; no code or data link was found in the paper bytes. · locator: candidate evidence excerpts · source: https://arxiv.org/html/2605.15199v1

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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