Using emerging artificial-intelligence techniques, stories can be processed into embeddings, into high-dimensional vectors, so that stories that are similar are mapped to vectors proximate to one another in story spaces.

A brief and recent history of story embeddings is presented and some of their applications are indicated below.

In 2020, Lee and Jung, with Story2Vec, focused on social networks among story characters (character networks), extending substructure-based graph embedding techniques.

In 2024, Benara, Singh, Morris, Antonello, Stoica, Huth, and Gao utilized large language models to ask targeted questions of texts while mapping responses to specific features.

In 2024, Hatzel and Biemann proposed a model, StoryEmb, which generated embeddings for stories such that similar stories, e.g., reformulations of the same story, resulted in similar embeddings.

In 2026, Mitka focused on the narrative comparison task using three narrative aspects: abstract theme, course of action, and outcome.

In 2026, Bigelow, Sarfati, Wurgaft, Lewis, McGrath, Merullo, Geiger, and Lubana explored mapping incrementally-processed stories into trajectories through conceptual belief spaces.

Story-based search involves inputting stories, instead of keywords, to retrieve resources. Users’ input stories could be processed into embeddings and, using vector databases, resources could be retrieved, sorted, and presented.

As envisioned, dialogue systems could engage in conversations with users to improve their understandings of users’ input stories in order to enhance story-based search.

Pieces of advice could be stored at one or more coordinates in story spaces and be retrieved when users’ input stories were similar. Using such systems, both users and artificial-intelligence agents could share pieces of advice with one another.

Users and artificial-intelligence agents could store wisdom materials at one or more coordinates in story spaces and retrieve them using story-based search techniques.

Kinds of wisdom materials include, but are not limited to: allegories, anecdotes, aphorisms, apologues, fables, folklore, humor, literature, lyrics, parables, poems, proverbs, quotations, songs, stories, and witticisms.

One could store story-space vector differences at coordinates in story spaces, vectors from story-space coordinates to subsequent coordinates. This would enable similarity-based lookups for story-prediction and -completion algorithms.

Such data could be obtained by processing collections of stories and vectors could potentially be stored with provenance data.

The technologies under discussion can enable new advanced forms of case-based reasoning and planning. Artificial-intelligence agents could retrieve resources from story spaces and store revised resources into such spaces.

Users and artificial-intelligence agents could store laws, rules, and regulations in story spaces and retrieve them using story-based search techniques.

Artificial-intelligence agents’ situational contexts, i.e., their autobiographical episodes, could be of use for retrieving situationally-applicable laws, rules, and regulations.

Available courses of action, then, could be efficiently considered with pertinent laws, rules, and regulations loaded into working memory.

Benara, Vinamra, Chandan Singh, John X. Morris, Richard J. Antonello, Ion Stoica, Alexander G. Huth, and Jianfeng Gao. "Crafting interpretable embeddings for language neuroscience by asking LLMs questions." Advances in Neural Information Processing Systems 37 (2024): 124137.

Bigelow, Eric, Raphaël Sarfati, Daniel Wurgaft, Owen Lewis, Thomas McGrath, Jack Merullo, Atticus Geiger, and Ekdeep Singh Lubana. "Stories in space: In-context learning trajectories in conceptual belief space." arXiv preprint arXiv:2605.12412 (2026).

Hatzel, Hans Ole, and Chris Biemann. "Story embeddings - Narrative-focused representations of fictional stories." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 5931-5943. 2024.

Lee, O-Joun, and Jason J. Jung. "Story embedding: Learning distributed representations of stories based on character networks." Artificial Intelligence 281 (2020): 103235.

Mitka, Jan. "Disentangled representation learning for narrative similarity using synthetic supervision." (2026).