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Humans Introduce, Models Elaborate: Asymmetric Narrative Agency in Human-LLM Co-Writing — arXiv:2609.07920

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.07920
License (as archived): CC0-1.0
Archived: 2026-10-16T13:46:45.530Z
SHA-256 of the archived text: 92a7ecbc20f230507a6dcb81f642a156b3a2af840d17b7b7deec39a33af68ac8
Stable URL of this page: https://metraipolis.com/documento/dc_04e85e87f62e

How to cite

Humans Introduce, Models Elaborate: Asymmetric Narrative Agency in Human-LLM Co-Writing — arXiv:2609.07920 — arXiv (en), https://arxiv.org/abs/2609.07920 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_04e85e87f62e (Metraipolis archive dc_04e85e87f62e, sha256 92a7ecbc20f230507a6dcb81f642a156b3a2af840d17b7b7deec39a33af68ac8)

BibTeX

@misc{dc04e85e87f62e,
  title = {Humans Introduce, Models Elaborate: Asymmetric Narrative Agency in Human-LLM Co-Writing — arXiv:2609.07920},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.07920},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_04e85e87f62e; sha256 92a7ecbc20f230507a6dcb81f642a156b3a2af840d17b7b7deec39a33af68ac8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.07920. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Human-LLM co-writing is increasingly used for open-ended text generation, but much prior work focuses on final outputs rather than the interactional dynamics through which stories are produced. We study turn-based collaborative storytelling across three matched conditions: Human-Human (HH), Human-LLM (HA), and LLM-LLM (AA). Using a shared storytelling paradigm, we measure how agents align, introduce novel material, and influence narrative development through turn-level measures of valence adaptation, semantic novelty, transience, and resonance. Our results show that HA co-writing is not intermediate between HH and AA collaboration. Instead, it displays a distinctive asymmetry where humans tend to introduce more novel and persistent narrative material, while LLMs tend to elaborate and stabilize the existing context. These findings suggest that, in this setting, LLMs function less as human co-authors and more as adaptive narrative amplifiers that reshape how agency is distributed in collaborative writing.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-09 in cs.HC).
Categorie arXiv: Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2609.07920 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T13:28:43.143Z dalla pagina https://arxiv.org/catchup/cs.HC/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).