Image JevBench v0.1

Public means an item was already exposed anywhere. That includes all 79 Mind2Web-derived items because Kev's training data overlaps Mind2Web; the 89 promo photos shown in videos; the 8 example cards on this page and in the status video; and 61 source rows in the public site repository since the 21 Sep preview. The split also counts 1 image asset already present in that repository. Every scored item that has never been exposed is sealed.

Sealed is 123 never-exposed v0.1 items plus 333 fresh items drawn from our private synthetic rotation pool (documents, charts, inventory and safety scenes, and everyday photos). The draw was stratified by family and difficulty with a seeded draw, and the split counts and hashes were frozen before any system saw a fresh item. Computer Use and Browser Use pool items were excluded because those tracks stay separate. Existing item-level outputs are reused for the public and kept sealed items; every system was run on the fresh items with the same code, pinned revisions, prompts and settings as its original run.

Retired. 93 items (ScreenSpot 45, ScreenSpot-Pro 31, Android-in-the-Wild 17) were moved from public to sealed on 24 Sep while their inputs and every system's predictions sat outside the sealed store, so they cannot count as an unseen holdout. They are not scored and not relabelled.

Intelligence. Accuracy counts missing, invalid and unparseable answers as wrong. Each part is chance-corrected against its own average chance rate, then combined as 35% public and 65% sealed. Calibration uses the same weights.

Matched-family overfit penalty. The gap is public accuracy minus sealed accuracy within families that have at least 10 items on both sides: ScreenSpot and Everyday photo. If that matched gap is above 15 percentage points, Intelligence is multiplied by max(0, 1 − (gap − 15)/100). The same rule applies to every system. The raw overall gap is shown in the data but does not affect the score.

Calibration. Ten-bin top-label ECE is scaled by valid probability coverage. Label-only output receives zero calibration. OpenJev's NLI entailment values select an answer but are not treated as categorical probabilities.

Speed and cost. These rules are unchanged. Speed uses whole-call p50 and p95 latency; local latency uses the v1.4 2× plus 0.15-second adjustment. Hosted unit cost uses returned per-call usage receipts; missing receipts are not zero-filled, and the Cost axis is scaled by receipt coverage. Retry costs are tracked separately. Local cost uses measured GPU seconds at the recorded per-system GPU-hour rate and excludes loading, downloads, build, and idle time. For self-hosted systems, the original run's timings are combined with the fresh-item run on the same GPU type.

Composite and gates. These rules are unchanged. The four axes use an equal-weight harmonic mean, followed by the Jev-class Intelligence, Speed, and Cost gates below 50. Gemini 3.8 Flash's high raw accuracy but near-zero composite reflects its measured cost and the Cost gate; label-only systems have zero Calibration under the inherited convention.

Difficulty balance. The split follows exposure, not a stratified draw, so the parts differ in family mix: browser actions (Mind2Web), chart questions (FinQA) and geometry are public-only, while ScreenSpot-Pro, Android-in-the-Wild and the fresh pool families are sealed-only. This is why the overfit penalty compares only matched families (ScreenSpot and Everyday photo).

Fresh-item difficulty. The 333 fresh sealed items are our own synthetic images and renders. They are easier for frontier API models than the older real-source items, so sealed accuracy is higher than on the earlier split for most hosted systems. Scores on this split are therefore not comparable with the earlier 228/216 preview.

Exposure. GPT-6 Luna and Gemini 3.8 Flash previously saw public promo-photo candidates and sealed-photo candidates in stateless label-check calls, including candidates later dropped. The checks showed no gold; human gold-blind adjudication decided inclusion. GPT-6 Luna is the saved low-reasoning-effort setting. Four OpenRouter systems used the requested no-retention route with fallback disabled; Gemma used the Autoloops endpoint and is API-flagged, with no no-retention claim made here. Local systems ran without network, credentials, or gold maps. The fresh pool items were authored with Claude and reviewed by OpenAI Codex as a blind critic, and the pool photos were generated with an OpenAI image model. This is disclosed for the two OpenAI rows. Self-hosted systems ran the fresh items on one rented GPU pod in offline containers with read-only inputs.