Index of everything on this site: https://ai.hiina.space/index.md
Both halves of a story autoencoder have prior art, but in our searches we found nobody who joins them. On the decompile side, film-to-screenplay systems exist: ScreenWriter (2024) works on whole films and OmniScript (2026) on 5 to 30 minute clips. There is also a long line of text-to-structure extractors for Propp functions, ATU tale types, Thompson motifs, turning points and TV Tropes. On the recompile side, plan-conditioned generators run from Plan-and-Write (2019) through Dramatron and DOC to Agents' Room (2025). What we found none of is a study that does the whole loop at story or screenplay scale: extract a structure from an existing story, regenerate from it, score the reconstruction against the source with controls, then edit the structure and score the edit.
The near-misses are worth knowing exactly. Shen & Ying compress Chinese web novels to outlines and expand them back, measuring distortion, but they make no edits and use no decoys. Chen & Si use story reconstruction as a feedback signal to refine annotation prompts (abstract only). Rewriting Video closes a video-to-text-to-video loop, but only on 8-second clips.
Across the separate pieces, the evidence points one way. Coarse, boundary-tolerant representations come back reliably: event and scene lists, and coarse type labels queried one at a time as a checklist. Named functions, exact beats, tropes, and causal or character-state links do not. GPT-4 reaches at best 27.1 F1 on 95 film tropes, against about 65 for humans. A 2019 model matches turning points 41% of the time on synopses, against 64% for humans. Decoders realise only about 72 to 78% of plan items, and the best book-scale claim checker reaches an F1 of 58.2. That makes decoy controls mandatory, for the same reason any LLM claim checker needs shifted controls. The edit side is thinner still: the only established benchmark edits the ending of a five-sentence story, and LLM "contemporary adaptations" of stories double or nearly quadruple their plot-hole rate. The project is open, cheap to start on text through the API, and its main methodological risk is that a decoder already knows Hamlet, so canonical works will appear to reconstruct from almost nothing.
Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space