arXiv:2606.25379v1 Announce Type: new Abstract: 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 $d=\bar{x}_{\rm seq}-\bar{x}_{\rm orig}$ 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 a
This research is emerging now due to advances in large language models and embedding techniques, making the geometric analysis of complex text transformations feasible.
This development offers a novel, quantifiable method for understanding creative processes and potentially automating content generation and transformation at a semantic level.
The ability to decompose creative transformations geometrically provides a new tool for content analysis, synthesis, and potentially intellectual property generation, moving beyond mere statistical resemblance.
- · AI researchers
- · Creative industries relying on AI
- · Content creators
- · Literary analysis platforms
- · Traditional content analysis methods
- · Bots generating simplistic content
This method allows for the precise identification and replication of 'story operators' that transform narratives.
It could enable AI models to generate sequels or adaptations that adhere closely to stylistic or thematic transformations learned from existing works.
This could lead to a new paradigm in intellectual property creation, where 'transformation patents' or similar concepts emerge for specific narrative operators.
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