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2026-09-20 concept structured-outputclassificationnon-autoregressivecalibrationrlcdopen-weightslocal-modelsllm

Jev / System One — Decision Models That Never Generate Text

Jev is TypeSafe AI’s flagship model (founder Diogo Almeida, ex-OpenAI, worked on the research behind ChatGPT) and the first of what they call System One models. It is not an LLM in the usual sense: non-autoregressive, it never generates tokens one by one. You send unstructured text as state plus typed questions — Choice (classify), Score (ordinal rate), Noul (probability of yes) — and it answers all of them in one parallel pass, each as a typed value with a probability distribution and a confidence score. No text means nothing to parse and nothing to hallucinate. Training is RLCD (Reinforcement Learning for Calibrated Decisions): the reward is epistemically honest probabilities, not human preference (RLHF) or verifiable answers (RLVR). Reported numbers: 70-500ms end-to-end, $0.042 per MTok input, output free; TypeSafe claims ~194x faster and ~445x cheaper than frontier LLMs on workflow benchmarks. Adding questions barely changes latency, since each is evaluated in parallel against the same state.

A real-world data point (reported, from a Telegram channel author’s test): classifying 4 MB of text (~1,597 posts) into categories took ~1 minute at 16 concurrent requests and cost $0.08.

Jev itself cannot be downloaded. Closed weights, hosted API only (api.typesafe.ai, early access), no self-host or on-prem path announced. The confidence calibration is the product.

The downloadable path: Laya

convaiinnovations/laya is an open reproduction with weights on Hugging Face, Apache 2.0, pip install laya:

Model-card claims (reported, not verified here): 7-8x faster than Jev per question, better typed-decisions accuracy (0.766 vs 0.727) and far better calibration after temperature scaling (ECE 0.081 vs 0.246); Jev still leads on high-cardinality choices (>20 options) and soft distribution matching. At 421M params this runs on a laptop — the entire hosted product collapses into a local pip install.

Measured here (Apple M5, 32 GB, 2026-09-20, ~/startups/active/laya-ane): the multilingual checkpoint scores 10/12 on a hand-labeled EN+RU smoke set — both failures are Russian, and one is confidently wrong (urgency Noul at 0.023 with 0.977 confidence), so RU output needs a human-review gate until measured on a bigger set. English: 9/9. Latency: 34 ms per 3-question call on MPS (matches the card’s ~33 ms). Ported to ONNX (int8: 325 MB, decisions unchanged) and Core ML: fp16 615 MB at 13.7 ms/question on the M5 GPU, int8 309 MB at 25.2 ms — 5-35x faster than hosted Jev’s 70-500 ms. The Neural Engine refused the model outright (ANECCompile() FAILED; the 34 ms “ANE” number is E5RT’s fallback); a transformer needs ane_transformers-style surgery to pass, and the GPU number made that not worth chasing. Five coremltools 9.0 blockers and their patches are in the repo’s README.

Other reproductions, all downloadable: razorback16/openjev (Jev-compatible server on DiffusionGemma, ~18 GB, vLLM), ekzhang/openjev-sglang (prefill-only trick over open models: read logits from the prefill pass, never decode), logan-markewich/jeff (GliFormer). Tracker: jev-reproductions-tracker on HF Spaces.

Why it matters

The insight is the class, not the vendor: for routing, triage, moderation, scoring — the bulk of production “AI” calls — generation was never the point. A model that only answers closed questions can be small, calibrated, and 100x cheaper, and a Noul at 0.55 can honestly route to a human where an LLM’s confident prose cannot. This fits privacy-first/offline-first products directly: Laya-multilingual at 647 MB is download-on-demand territory for a desktop app.

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