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Story Operators: Decomposing the Original → Sequel Transformation in Embedding Space

arXiv 2606.25379 ↗First posted 24 Jun 2026cs.CLcited by 0

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Abstract (arXiv)

I treat a book as a point in a sentence-embedding space and a literary transformation as an operation on points. Given an original novel and its sequel, I ask what it takes, geometrically, to turn the first into the second. Using all-mpnet-base-v2 paragraph embeddings drawn from a precomputed index of the PG19 corpus, I form the displacement d=barxrm seq-barxrm orig and greedily decompose it along a content basis obtained by PCA over the two books' own paragraphs. Each component is an interpretable axis anchored by real passages at its poles. Across thirteen verified author pairs from Project Gutenberg, the decomposition reveals a small taxonomy of sequels: formulaic (a tiny, low-rank change: Doyle's Holmes collections, |d|=0.12), concentrated (one dominant axis: Alcott's Little Women → Little Men, 75% on a single move), and compositional (many small axes: Twain, Burroughs's Barsoom, Nesbit). For the canonical case, Tom Sawyer → Huckleberry Finn, the dominant recovered axis is structural -- the collapse of sheltering domesticity into a picaresque road -- rather than the famous surface themes of vernacular voice or slavery, which ride later, smaller axes; and the transformation routes through adventure-journey space rather than diluting toward generic realism. I corroborate the recovered geometry against Twain's documented authorial intent (his 1875--76 letters to Howells), which names the first-person picaresque move years in advance, and I quantify, with an explicit representation caveat, how much of the realized transformation his stated intentions span. All computations are reproducible from the released scripts and data.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 5 references that are in the map. First posted on arXiv 24 Jun 2026.

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References · 3

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 981
  3. 2019Compressive Transformers for Long-Range Sequence Modellingcited by 726

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