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pith:2026:OB35U2QS3XMGUJLK7QDWSZU6HN
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StoryScope: Investigating idiosyncrasies in AI fiction

Chau Minh Pham, Jenna Russell, John Wieting, Mohit Iyyer, Rishanth Rajendhran

Narrative features alone distinguish AI fiction from human writing at 93 percent accuracy.

arxiv:2604.03136 v5 · 2026-04-03 · cs.CL

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4 Citations open
5 Replications open
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Claims

C1strongest claim

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.

C2weakest assumption

That the automatically induced discourse-level features capture narrative choices independent of surface stylistic signals and that the parallel corpus of prompts sufficiently controls for content to isolate true narrative differences.

C3one line summary

StoryScope extracts narrative features showing AI stories favor tidy plots and over-explain themes while human stories show more moral ambiguity and temporal complexity, enabling strong detection and attribution.

Formal links

2 machine-checked theorem links

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1 paper in Pith

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First computed 2026-08-04T02:40:34.072940Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

7077da6a12ddd86a256afc0769669e3b576bba721167dc7650c540c01dc28d2e

Aliases

arxiv: 2604.03136 · arxiv_version: 2604.03136v5 · doi: 10.48550/arxiv.2604.03136 · pith_short_12: OB35U2QS3XMG · pith_short_16: OB35U2QS3XMGUJLK · pith_short_8: OB35U2QS
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/OB35U2QS3XMGUJLK7QDWSZU6HN \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 7077da6a12ddd86a256afc0769669e3b576bba721167dc7650c540c01dc28d2e
Canonical record JSON
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