# What do openly readable empirical studies report about how readers or models track a character's changing traits through a story, and what remains unmeasured?

- id: art_01M5MV3R0V01SZ5718RBD9Z5PM
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T01, T20 · tags: none
- updated: 2026-10-23T16:57:07.348Z
- url: https://veyrul.com/articles/art_01M5MV3R0V01SZ5718RBD9Z5PM

No open-access experiment that directly manipulates character change and measures reader perception was found (bounded to the searches listed). What open papers do report: PersoNet (ACL 2023) builds a dataset of readers' notes about characters' traits at a reading position and finds that humans who know the book history reach more than 70% accuracy versus about 45% for the best model, and that oracle history traits are limited because characters' personalities evolve; BookWorm (EMNLP Findings 2024) finds models handle static aspects (role, personality) better than dynamic ones (events, relationships); TimeChara (2024) shows role-playing LLMs break point-in-time consistency. These are NLP benchmark results on proxies, not direct tests of perceived transformation.

No open-access experiment that directly manipulates character change and measures reader perception was found (bounded to the searches listed). What open papers do report: PersoNet (ACL 2023) builds a dataset of readers' notes about characters' traits at a reading position and finds that humans who know the book history reach more than 70% accuracy versus about 45% for the best model, and that oracle history traits are limited because characters' personalities evolve; BookWorm (EMNLP Findings 2024) finds models handle static aspects (role, personality) better than dynamic ones (events, relationships); TimeChara (2024) shows role-playing LLMs break point-in-time consistency. These are NLP benchmark results on proxies, not direct tests of perceived transformation.

Curator qualifications: All excerpts located; empirical trait-change tracking studies.

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/2305.10156v3",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/abs/2405.18027v1",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://aclanthology.org/2024.findings-emnlp.258.pdf",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] No open-access experiment that directly manipulates character change and measures reader perception was found (bounded to the searches listed). What open papers do report: PersoNet (ACL 2023) builds a dataset of readers' notes about characters' traits at a reading position and finds that humans who know the book history reach more than 70% accuracy versus about 45% for the best model, and that oracle history traits are limited because characters' personalities evolve; BookWorm (EMNLP Findings 2024) finds models handle static aspects (role, personality) better than dynamic ones (events, relationships); TimeChara (2024) shows role-playing LLMs break point-in-time consistency. These are NLP benchmark results on proxies, not direct tests of perceived transformation. · locator: candidate evidence excerpts · source: https://arxiv.org/abs/2305.10156v3

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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