Less to generate.
More to decide.
Give a model context and explicit questions.
Get typed probabilities over the choices you provide.
Can I get a refund?”
Watch complete games.
Selected successful episodes, from the opening state to the goal. Replayed actions from the original Qwen3.5-9B pilot.
A growing set of
inspectable decision tasks.
Prepared corpus inventory. The three new community control datasets are independently audited, with no training or model evaluation yet. Completed 2B/9B results below use the earlier release-v2 mixture.
A small vocabulary.
A useful way to think.
Choose an action. Test a condition. Score a criterion. Ask independent questions over the same context, with outputs you can use directly.
{
"state": "My order arrived damaged. Please refund it.",
"questions": {
"route": {
"type": "choice",
"instructions": "Which team should handle this?",
"criteria": {
"billing": "Refunds and charges",
"engineering": "Software defects"
}
}
}
}The candidates are yours. The model assigns probabilities; your application decides what to do next.
Direct probability decisions, without autoregressive text generation or parsing generated JSON. Independent implementation inspired by Jev; no claim to reproduce its proprietary method or speedups.
Decisions in motion.
Browse the selected demos across our task categories. Successful examples and interface walkthroughs; see Results for the full evaluation.
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No matching decisions.
Try another task name or category.
These clips make the interface inspectable. A replay of one response is not an end-to-end success rate; an interface walkthrough is not a model capability result.
Coverage still in progress
How fast is 2B?
Measured requests from the released Open-Jev-2B checkpoint. Inspect the workload, timing boundary and every raw attempt.
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All providers receive the same saved state and question semantics. The 2B numbers use a warm H100 with cache off and loopback HTTP. Hosted APIs use a fresh HTTPS connection per request, including Internet transit, TLS, routing and scheduling. These are client-observed request times, not a matched-hardware model speedup.
Swipe the table horizontally to compare providers →
Local timing boundaries and experimental cache diagnostics
| Workload | State tokens | Candidates | Cache off | Experimental cache | Validation |
|---|
For context, TypeSafe's launch article ↗ reports 70–500 ms end-to-end for Jev. That vendor range uses unspecified workloads and is separate from our measurements above.
Watch a measured request.
One recorded model response and the full timing distribution for that workload. Playback length is presentation time; the measured request time is printed on screen.
Exact request and recorded response
Show the work.
Keep the limits.
Full-data evaluation on every release-v2 test and OOD decision row. Independently audited, with failed, missing, and duplicate predictions checked.
Read the evaluation reportJF100 is a separate holdout.
100 questions × 3 option rotations, excluded from training. Final-model JF100 results remain pending.
| Model | Test | OOD | Status |
|---|---|---|---|
| Qwen3.5 2B | 94.71% | 86.02% | Audited |
| Qwen3.5 9B | 97.54% | 91.97% | Audited |
| Qwen3.8 27B | — | — | Training |
Each completed model: 26,452 held-out decision rows, including 25,492 hard-label rows used for accuracy. Test: 10,046 hard labels; OOD: 15,446.
These are decision-row metrics, not gameplay or workflow completion rates. No full-data base-model comparison was run. Final-model closed-loop results remain pending.
Aggregate scores hide weak subgroups: 9B Wiki OOD expected accuracy is 32.74%, and reasoning OOD hard-label accuracy is 73.60%. T-Rex test has only 4 rows. All source-level results remain available.
Download verified resultsBuild a smaller
decision loop.
Inspect the contracts, follow the evidence, and make the next choice explicit.
Model packages require their pinned upstream Qwen base weights. The dataset card documents redistribution exclusions and reconstruction of the original training mixtures.