[2604.03136] StoryScope: Investigating idiosyncrasies in AI fiction

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Computer Science > Computation and Language

arXiv:2604.03136 (cs)

[Submitted on 3 Apr 2026 (v1), last revised 13 Apr 2026 (this version, v4)]

Title:StoryScope: Investigating idiosyncrasies in AI fiction

Authors:Jenna Russell, Rishanth Rajendhran, Chau Minh Pham, Mohit Iyyer, John Wieting<br>View a PDF of the paper titled StoryScope: Investigating idiosyncrasies in AI fiction, by Jenna Russell and 4 other authors

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Abstract:As AI-generated fiction becomes increasingly prevalent, questions of authorship and originality are becoming central to how written work is evaluated. While most existing work in this space focuses on identifying surface-level signatures of AI writing, we ask instead whether AI-generated stories can be distinguished from human ones without relying on stylistic signals, focusing on discourse-level narrative choices such as character agency and chronological discontinuity. We propose StoryScope, a pipeline that automatically induces a fine-grained, interpretable feature space of discourse-level narrative features across 10 dimensions. We apply StoryScope to a parallel corpus of 10,272 writing prompts, each written by a human author and five LLMs, yielding 61,608 stories, each ~5,000 words, and 304 extracted features per story. Narrative features alone achieve 93.2% macro-F1 for human vs. AI detection and 68.4% macro-F1 for six-way authorship attribution, retaining over 97% of the performance of models that include stylistic cues. A compact set of 30 core narrative features captures much of this signal: AI stories over-explain themes and favor tidy, single-track plots while human stories frame protagonist' choices as more morally ambiguous and have increased temporal complexity. Per-model fingerprint features enable six-way attribution: for example, Claude produces notably flat event escalation, GPT over-indexes on dream sequences, and Gemini defaults to external character description. We find that AI-generated stories cluster in a shared region of narrative space, while human-authored stories exhibit greater diversity. More broadly, these results suggest that differences in underlying narrative construction, not just writing style, can be used to separate human-written original works from AI-generated fiction.

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Computation and Language (cs.CL)

Cite as:<br>arXiv:2604.03136 [cs.CL]

(or<br>arXiv:2604.03136v4 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2604.03136

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arXiv-issued DOI via DataCite

Submission history<br>From: Jenna Russell [view email]<br>[v1]<br>Fri, 3 Apr 2026 15:56:38 UTC (2,053 KB)

[v2]<br>Mon, 6 Apr 2026 01:44:49 UTC (2,052 KB)

[v3]<br>Wed, 8 Apr 2026 13:25:18 UTC (2,052 KB)

[v4]<br>Mon, 13 Apr 2026 20:04:18 UTC (2,045 KB)

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