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Jev glossary

PrimitivesBox questionBounding box

Box

The experimental Jev 1.14 question type that answers where something is in an image: four edges as fractions of the image, plus a confidence.
Category
Primitives
Also known as
Box question, Bounding box
Reading time
1 min
Related terms
4
Directory entries
4
Docs
docs.typesafe.ai
Added
2026-10-15

Definition

A Box question returns one bounding box — the smallest rectangle containing the target — as top, bottom, left, and right edges, each from 0 to 1 measured from the top-left corner, so the same answer is correct at any image size. It takes no criteria: instructions describe what to find and in which image, and may be a phrase like the coffee mug in photo.

Two design details matter. Box needs jev-1.14.0 or jev-preview plus the images header, and it has no not-found value, so an absent target still returns four edges; the documented pattern pairs Box with a Noul presence check and optional per-edge questions when a crop must not cut the target off. TypeSafe marks it dreamer-ready: format, limits, and accuracy can change between releases.

Tagsprimitivesvisiondanger-zone

From the Jev directory

Directory entries that go deeper on this term.

The box question type: one bounding box per question, returned as four edges that are fractions of the image, plus confidence, with no criteria field and no not-found value.
Jev Practices & PatternsDocs#official#docs#vision
Official
The Jev Day release post: Jev 1.14.0 under jev-preview plus three danger-zone features — composable images on both models, the box question type on 1.14 only, and opt-in 262k context.
Jev Guides & ArticlesArticle#official#release#vision
Official
0GitHub stars
0xRinne's MIT-licensed field report on using TypeSafe Jev 1.14's Box type and image input to spot AI-image hallucinations, across seven pipeline versions v1-v7b, with a live Vercel demo and raw score JSON.
Jev Tools & CLIs#community#research#hallucination-detection
Encodes an image and public context once, runs isolated question suffixes as a batch, and reads candidate probabilities from the backbone's language-model head. Answer-supervised post-training lifts equal-weight macro accuracy from 70.6% to 76.1% across four benchmarks.
Jev Models & ReimplementationsArticle#community#research#arxiv

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