# Directional Alignment and Narrative Agency in Human-LLM Co-Writing

arXiv: https://arxiv.org/abs/2604.23676 · HTML: https://mlsanity.com/paper/2604.23676/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2026.

## Built on by (most cited first, 3 of 3)

- [Same Lesson, Different Story: Cross-Lingual Reconstruction of Cultural Narratives in Large Language Models](https://mlsanity.com/paper/2606.24610/) (2026)
- [Designing Proactive Thought Partners for Writing](https://mlsanity.com/paper/2609.01588/) (2026)
- ["Looking for Something Weird to Happen": How Humans Sustain AI Agent Novelty Amid Semantic Collapse](https://mlsanity.com/paper/2609.16051/) (2026)

## Abstract (arXiv)

We investigate narrative agency in human-LLM creative co-writing, asking who drives story development in turn-based collaboration. Using a new corpus of 87 human-LLM co-written stories, we apply sentiment and semantic modeling to quantify affective alignment and semantic novelty in turn-taking, and directional measures to assess which agent shapes narrative progression. Our results show asymmetric influence: human turns introduce greater semantic novelty and are more likely to shape subsequent developments, whereas LLM contributions predominantly elaborate on human-introduced elements. At the sentiment level, alignment is also asymmetric, but more bidirectional: LLMs exhibit stronger turn-level emotional adaptation than humans, but both agents track each other's emotional valence and LLMs show an independent tendency to more positive emotional baselines. These findings indicate a complementary division of labor in human-LLM co-writing, where humans drive narrative innovation and direction, while LLMs act as adaptive amplifiers that sustain coherence and elaborate emerging narratives.