OpenJev

Most agent decisions are small: route this, retry that, does the evidence support X? A chat model can answer them, but it spends time generating text that software immediately parses back into an if statement.

Jev is TypeSafe’s closed service for runtime-defined semantic decisions. This project reproduces that interface pattern with open models; it does not reproduce Jev’s undisclosed model or training.

This baseline reads typed option probabilities directly from a model. No answer sentence, JSON repair, or decoding loop.

Quick start

Python 3.10+, CUDA, and a GPU that can hold a 4B BF16 model:

python -m venv .venv
. .venv/bin/activate
export HF_HOME=/path/to/large-drive/huggingface
pip install -e '.[test]'

Run the owned examples:

CUDA_VISIBLE_DEVICES=0 openjev-score \
  --mode direct \
  --model Qwen/Qwen3.5-4B \
  --revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a \
  --input examples/decisions.jsonl \
  --output results.jsonl

Each result contains typed option scores, timing, the exact model revision, and a prompt hash.

If every row has the same exact state, switch to --mode shared to prefill it once and evaluate the criteria in parallel.

How it works

flowchart LR
    S[Unstructured state] --> M[4B model]
    C[Runtime criteria] --> M
    O[Typed options] --> M
    M -- native option logits --> P[Probabilities]

• Runtime-defined: criteria and option descriptions arrive with the request.
• Decision-native: one forward pass reads declared option logits; no answer token is sampled.
• Shared-state aware: one long state can be prefetched once, then branched across many criteria.
• Auditable: the owned fixture, exact runners, row-level outputs, revisions, prompts, and known failures are committed.

Speed

Decisions versus a compact generated array

Same frozen Qwen3.5-4B, same owned state, same 21 binary criteria, one RTX 3090:

The compact generative baseline emits only ordered "yes"/"no" values—no keys, confidence objects, or explanations. Its median first-token time was 0.489 s, but completing the array took 5.21× as long as direct readout. All three arrays were valid and identical. Their choices agreed with direct argmax on 18/21 criteria, so this is a systems comparison rather than a claim that the two readouts are semantically equivalent. Exact prompt, outputs, token timeline, and runs are committed.

Reusing a state across 21 decisions

On an owned 37-state × 21-criterion workload:

The owned 37×21 fixture, direct/reuse runner, reranker runner, raw timings, and row-level predictions are included. The fast reuse paths are experimental: BF16 execution changed 5–6 of 777 argmaxes relative to fresh scoring.

Quality

Browser model ladder

Native BF16 scores. Browser builds use quantized GGUF. Jev is TypeSafe’s published result on the same 102-row subset.

General decision baseline

The reranker remained strong at retrieval ranking, but direct logits were the better general-decision baseline.

The Jev number is read from TypeSafe’s published records; we did not run a live Jev endpoint. The comparison covers the 102 rows that could be aligned from public artifacts, not TypeSafe’s reported 711-row aggregate.

Input

{
  "id": "route-1",
  "state": "Customer cannot access an account after a password reset.",
  "question": "Which queue should handle this request?",
  "options": [
    {"id": "access", "description": "Account access support."},
    {"id": "billing", "description": "Billing support."}
  ]
}

Returned probabilities are conditional on the supplied options. Calibrate and validate them on the workload where they will make decisions. state may also be a nonempty JSON object or array. Direct modes preserve it as structured JSON; reranker mode renders it as document text.

Documentation

• Results — quality, speed, perturbations, and claim boundaries
• Method — frozen prompts, metrics, and timing scope
• Reproduce — exact environment, pinned commands, perturbations, and verification
• Interactive replay
• Browser-only WebGPU demo — no waitlist; use it today
• Machine-readable summary
• Benchmark bundle — fixtures, runners, selection IDs, and reproduction commands
• Raw results and checksums
• Third-party sources

Star history

Evaluation sources

• TypeSafe public evaluations — public comparison cases used for selected-subset agreement
• Every parallel judgment lab and its downloadable experiment data
• WANLI — external natural-language inference check
• Qwen3-0.6B, MiniCPM5-2B, Qwen3.5-4B, and Qwen3-Reranker-4B — frozen baseline models

This is an independent research project. Model weights and third-party records without a redistribution grant are excluded; immutable selection IDs and fetch manifests are included. Upstream models retain their licenses. Project code is released under the MIT License.