01
Average trajectory by genre
Each story is cut into 40 equal narrative-progress bins, its LLM suspense (Qwen3-8B) series is z-scored within the story and lightly smoothed, then averaged over stories. Bands are 95% bootstrap intervals over stories.
Select one group to see its 95% bootstrap band over stories. Curves are z-scored within story before averaging, so height differences between stories are removed and only shape remains.
Continue to aggregate patterns for eras, story lengths, authors, clustering and the tests behind the genre claim.
02
Every story is an instrument
Hover a point on a story’s curve and the passage it measures lights up; hover the text and the point answers. Feature overlays show what the rating is made of.
03
Where to read
- Features — which lexical, semantic, narrative and model-based features co-move with suspense, and how the six measures agree.
- Prediction — leakage-safe held-out models (by story and by author), interpretability, and how early future suspense is predictable.
- AI stories — the separate generative experiment: do instructed stories follow their requested suspense trajectories?
- Annotate — take part in the human rating study (about ten minutes).
- Methods and the paper — full protocol, robustness, limitations, and the Reviewer-2 pass.