The full directory
All Jev AI model resources
Entries for TypeSafe AI's flagship System One model — official docs and SDKs alongside community clients, tools, and write-ups.
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All 3919 Jev AI model resources
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Showing 1633–1680 of 3919 resources
LAVOIR extends the Laya single-pass decision encoder so one forward pass returns both the decision distribution and, for every candidate missing slot, the expected gain from asking the user about it. Its question policy matches a greedy oracle on seen schemas and is 14.1 points more accurate than never asking.
Jev Models & ReimplementationsArticle#community#research#arxiv
Community
moganai/lavoirPython
moganai/lavoirPythonJev-Mobile moves mobile GUI agents to low-frequency VLM planning and high-frequency lightweight execution: the VLM specifies local goals, the accessibility tree defines a structured action space, and Jev selects actions. On AndroidWorld it reaches 79% task success against 78% for SeeAct-V.
Jev Tools & CLIsArticle#community#research#arxiv
Community
An evaluation of JEV-as-a-Judge, a decision-only judge that returns label probabilities and uses its confidence to accept a verdict or escalate to a reasoning judge. Against sixteen generative and reward-model judges it comes within three points of GPT-6 where a verdict can be read off the text, at 0.36% of its fee.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
REFLEX is an agent architecture that uses Jev as a fast, typed decision layer and calls a strong LLM only when confidence is low or generation is required. On a frozen 100-task benchmark it reaches 95% success with 72.7% fewer strong-model calls than a strong-only agent.
Jev Agents & MCPArticle#community#research#arxiv
Community
Jev-Mem is an agentic memory architecture that gives a System-One control plane charge of memory typing, query routing, retrieval budgets, graph traversal, candidate scoring, and adaptive stopping, invoking System Two only for complex reasoning and synthesis. On LoCoMo it scores 0.777.
Jev Tools & CLIsArticle#community#research#arxiv
Community
An empirical study of whether Jev's decision-level savings transfer to task-level gains, across five agents in ALFWorld and AndroidWorld. Across 4,500 task executions, decision cost and latency fall in all five agents, but success declines in two mobile agents and per-outcome cost rises in one.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
JevSpawn, a compositional policy, connects natural-language task specifications to finite probabilistic exploration: parallel action spawning pairs with feedback-driven branch selection, representation revision, and recovery from retained alternatives, with no training. It improves performance on eight benchmarks.
Jev Agents & MCPArticle#community#research#arxiv
Community
An independent reproduction of the shipped Laya Typed-Decisions checkpoint, a 421M-parameter ModernBERT-large assessor, finding it uniformly under-confident rather than over-confident. A single fitted temperature removes most miscalibration, while the preregistered isotonic selection failed its held-out contrast.
Jev Benchmarks & EvaluationsArticle#community#research#arxiv
Community
AutoTrust's bf16 System 1 decision model on Qwen3.5-35B-A3B with about 3B active parameters: Decision Index 0.3 public 59.73, ERQA 51.3% in one pass with zero robot training, 66.5 GiB of weights.
Jev Models & Reimplementations#community#open-models#local
Community
AutoTrust's combined System 1 and System 2 bundle on Gemma-4-26B-A4B-it: Decision Index 0.2.1 of 58.05, a 24-slot head with noul, choice and score, and the base chat model left bit-identical.
Jev Models & Reimplementations#community#open-models#local
Community
akhilaaa3's merged multimodal decision classifier on Gemma 4 12B IT: probabilities for text, image, audio or video options, 87.57% on DecisionBench Medium and MMAU 63.10%, Apache-2.0.
Jev Models & Reimplementations#community#open-models#local
Community
Reza2kn's llama.cpp Q4_K_M build of Jev-Omni: a 6.87 GiB multimodal decision backbone plus projector and FP32 head, measured at 8.98 GiB VRAM on an RTX 5080 Laptop GPU.
Jev Models & Reimplementations#community#open-models#local
Community
Ruiruiz30's Apple Silicon MLX conversion of Jev-Omni: 4-bit affine language weights on a Mac mini M4 16GB, about 7 GB peak Metal memory and roughly 963 ms text decisions, Apache-2.0.
Jev Models & Reimplementations#community#open-models#local
Community
alibiserikbay's Apache-2.0 Jev alternative on Qwen3.5-4B with a merged rank-16 LoRA: one forward pass returns a probability per option, 0.784 on JevBench's public hard tier at about 13 ms per decision.
Jev Models & Reimplementations#community#open-models#local
Community
The 9B JevK5: the same v0.3 data on Qwen3.5-9B, retrained for 1.5 epochs by a pre-registered sweep, reaching index proxy 0.794 and a 0.859 hand-written hard set, Apache-2.0.
Jev Models & Reimplementations#community#open-models#local
Community
chaoliangUNSW's v1 Jev-Style decision model in GGUF from 1.3 GB: calibrated choice, bool and score answers on Qwen3.5-2B-Base, 82.3% on five held-out task families with ECE 0.017.
Jev Models & Reimplementations#community#open-models#local
Community
An Apple Silicon MLX bf16 build of the v1 Jev-Style decision model: 77 ms decisions on an M1 Max with calibrated choice, bool and score probabilities, Apache-2.0 weights.
Jev Models & Reimplementations#community#open-models#local
Community
The v3 Jev-Style decision model at 2B in GGUF: F16 3.78 GB and Q8_0 2.01 GB match PyTorch FP32 on 100% and 99.7% of parity rows, 73.6% on JevBench v1.4.1 public items.
Jev Models & Reimplementations#community#open-models#local
Community
Mithril's open, Jev-shaped typed-decision model on DeBERTa-v3-large: choice up to 255 options, score and noul from one forward pass, 0.854 in-domain with ECE 0.022, trained on public gold labels only.
Jev Models & Reimplementations#community#open-models#local
Community
A Transformers.js ONNX conversion of open-jev-deberta-v3-large: fp16, fp32, q4 and q4f16 graphs from 0.35 GB that score typed questions in the browser through a single forward pass.
Jev Models & Reimplementations#community#open-models#local
Community
TokenRhythm's 4B prefill-only decision model built on NeoHorse-1-4B: given a state and application-defined questions it returns Choice, Noul, and Score probabilities without generating text, reporting a 77.70 six-group aggregate under Apache-2.0.
Jev Models & Reimplementations#community#open-models#local
Community
A Qwen2.5-1.5B-Instruct fine-tune that emits typed dependency verdicts, safe, caution, or block with a recommendation, for Dependabot and Renovate pipelines, reporting 95.7% F1 on the card's 4,800-update OSV-Bench.
Jev Models & Reimplementations#community#open-models#local
Community
chenz53's standalone decision model derived from Google Gemma 4 26B A4B IT: full merged BF16 weights, a fitted temperature of 1.736507, and 79.50% equal-type accuracy over 1,100 final questions.
Jev Models & Reimplementations#community#open-models#local
Community
A CPU-first BitNet student: Microsoft's BitNet b1.58 2B quantized to an I2_S GGUF with a float32 pointer head, distilled from Kev 9B logits and run through the bit-jev Python package with no separate open-weight licence.
Jev Models & Reimplementations#community#open-models#local
Community
A LoRA adapter for Qwen3-VL-4B-Instruct that scores supplied visual answer candidates instead of writing prose: macro accuracy over four choice benchmarks rises from 0.706 to 0.761, Apache-2.0.
Jev Models & Reimplementations#community#open-models#local
Community
An early step-10,626 checkpoint of a 0.6B agentic decision model on Qwen3-0.6B that scores supplied tool and action candidates, reaching 96.5% on a BFCL V1 function-selection adaptation under Apache-2.0.
Jev Models & Reimplementations#community#open-models#local
Community
A 3.74B language-diffusion decision model that answers 184 of 231 matching adaptation questions (79.65%) against Jev 1.13.0's public 86.58%, matching it on the easy subset at 33 ms p50 on one RTX 5090.
Jev Models & Reimplementations#community#open-models#local
Community
An unofficial cross-encoder fine-tune of ModernBERT-ja-310m for Jev Local: it reads a question-plus-state and one candidate per sequence and hits 92.62% on the JGLUE JNLI test, with CC BY-SA 4.0 weights.
Jev Models & Reimplementations#community#open-models#local
Community
A 0.6B non-autoregressive decision model on Qwen3-0.6B that returns calibrated Choice, Score and Noul distributions in one forward pass, scoring 95.8% on its 22,110-item held-out test split.
Jev Models & Reimplementations#community#open-models#local
Community
AgentJev-0.6B scores coding completion with a Qwen3-0.6B backbone stripped of its LM head: 57.8% accuracy (606/1048) on the coding set and 87.2% agreement with the teacher on invoice questions.
Jev Models & Reimplementations#community#open-models#local
Community
A Qwen3-Embedding-0.6B fine-tune merged from LoRA that raises JevEmbed-Data test accuracy from 33.79% to 82.30%, producing 1,024-dimensional embeddings for Choice, Score and Noul decisions.
Jev Models & Reimplementations#community#open-models#local
Community
The 322M mmBERT-base build of Laya for 100+ languages: state plus typed questions return typed answers with probabilities in one forward pass, with up to 8,192 tokens for long documents.
Jev Models & Reimplementations#community#open-models#local
Community
ggmlc-compiled GGUFs of the Laya typed-decisions specialist on ModernBERT-large: F16, Q8_0, and UD_Q4_K_M files for invoice, security, customer-service, and harness presets, Apache-2.0.
Jev Models & Reimplementations#community#open-models#local
Community
An EmbeddingGemma 2 fine-tune that answers choice, score, and noul questions from a state and a plain-data schema, with a 0.5M-parameter schema-conditioned scorer over 768-d embeddings.
Jev Models & Reimplementations#community#open-models#local
Community
Gemma 3 270M fully fine-tuned as a probabilistic decision model: 0.761 accuracy and 0.1319 KL on the 400-case typed-decisions test set, reading letter-token logits one question per pass.
Jev Models & Reimplementations#community#open-models#local
Community
An Open Classification Foundation smoke checkpoint: a 164M-parameter ModernBERT-base with a cross-entropy decision head, 0.751 accuracy and 0.044 ECE on 2,000 held-out typed decisions.
Jev Models & Reimplementations#community#open-models#local
Community
An open Thai + English System One decision model at 0.8B, Apache-2.0: choice with up to 255 options, fractional score, and noul answers in one forward pass over a Qwen3.5-0.8B text tower.
Jev Models & Reimplementations#community#open-models#local
Community
A Core AI conversion of OpenThai-SystemOne for Apple silicon: int8lin and fp16 .aimodel bundles with a 4,096-token context and 256-wide slot logits, gated against the author's fp32 oracle.
Jev Models & Reimplementations#community#open-models#local
Community
A local System One-compatible decision model on Qwen2.5-0.5B-Instruct: 50.7% accuracy and 0.245 ECE on JevBench public with a 13.1 ms median, built for demos rather than production.
Jev Models & Reimplementations#community#open-models#local
Community
A LoRA scorer on Qwen3.5-4B-Base that scores (state, question, option) triples with a scalar head and softmaxes per question, reaching 0.707 accuracy and 0.044 ECE at T=1.75.
Jev Models & Reimplementations#community#open-models#local
Community
An experimental decoder-based System One engine: Qwen3-1.7B with a constrained answer-token readout that answers the /v1/systemone wire format and can escalate to a short reasoning trace, Apache-2.0.
Jev Models & Reimplementations#community#open-models#local
Community
A 70.8M-parameter DeBERTa-v3-xsmall cross-encoder that answers typed decision questions in one batched pass with zero generated tokens, shipped with a calibration.json that the engine requires.
Jev Models & Reimplementations#community#open-models#local
Community
The largest of Bekko's v0 encoder models for Choice, Noul, and Score decisions: 395M parameters built on an Ettin reranker, with shared-prefix encoding and a 1.43 GB browser ONNX export.
Jev Models & Reimplementations#community#open-models#local
Community
A 4B decision model taking text, JSON, or images up to 262,144 tokens: 52.75 on Decision Index 0.2.1, 204 of 231 on JevBench public, and 26.0 ms for a three-question request on one H200.
Jev Models & Reimplementations#community#open-models#local
Community
A decision model that scores its own clarifying questions by value of information: at AUC .799 against an oracle .797, it asks only when asking is expected to change the answer, then decides or hands off.
Jev Models & Reimplementations#community#open-models#local
Community
Real human-labeled data reformatted into System One questions: 22 configs, 166,054 rows and 22,773 test records where choice, score and noul answers are scored as probability distributions.
Jev Benchmarks & EvaluationsDocs#community#benchmark#evaluation
Community
Complete Decision Index 0.2.1 runs of AutoTrust's JEV typed-decision models: engine outputs for JEV-9B, JEV-27B and JEV-Gemma4-26B-A4B, each covering 150,317 scoreable requests with zero errors.
Jev Benchmarks & EvaluationsDocs#community#benchmark#evaluation
Community
Twelve million canonical agent-decision records built from public agent trajectories, canonicalizing state plus candidate decisions into target, outcome and eligibility supervision under CC BY 4.0.
Jev Benchmarks & EvaluationsDocs#community#benchmark#evaluation
Community
Start here with the Jev AI model
Hand-picked entries the maintainers recommend first, from official primitives to community tools.
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
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
LiteLLM's Day 0 post for TypeSafe Jev 1.14: the proxy adds box questions, image input and long-context mode on both POST /v1/systemone and POST /v1/decisions, forwarding TypeSafe-Danger-Zone headers only to TypeSafe deployments.
Jev Integrations & AdaptersArticle#community#litellm#gateway
Community
Newest Jev AI model resources
The latest additions to the directory, newest first.
Do Multimodal Models Decide Consistently on Equivalent Optical Spectra?Jev Benchmarks & Evaluations
@harlan-zw/jevJev Repos & SDKs
Jev-AD: Safe Language-Guided Autonomous Driving via a Language World ModelJev Demos & Experiments
Jev GUI Training PreviewJev Models & Reimplementations
- jev-playgroundJev Demos & Experiments
- JeviewJev Tools & CLIs
From the Jev community
Watch walkthroughs, read the threads, and see what builders are posting.
YouTube: KodeKloud's three-minute primer: what a System One model decides, and why it answers in milliseconds.
YouTube: Caleb Writes Code explains what Jev is and how to call it, in seven minutes.
YouTube: Greg Isenberg and Ryan Vogel on the businesses Jev unlocks, and a path in through the Vercel AI Gateway.
YouTube: CJ from Syntax demos browser use, classification, code review, a model router, and a chat bot with no LLM.
YouTube: Ryan Vogel runs Jev on 100 and then 1,000 emails: category, priority, spam, and reply predictions.
YouTube: AICodeKing tests support routing, refund detection, prompt-injection resistance, and browser automation.
Hacker News: Show HN: Ad Spend Guard gates budget before the auction, not the creative after.
Show HN: Gating ad budget with a decision modelRead the thread on Hacker NewsReddit: Reranking rebooking options with a decision model, ranked with reasons.
Hacker News: Pick Verify: a single decision at the warehouse pick station catches confusable picks.
Show HN: Verifying robot picks with a decision modelRead the thread on Hacker NewsHacker News: Decision Index 0.2.1 scores open reproductions of Jev on a frozen suite.
jackbravo4 pointsDecision Index 0.2.1 – 70 open reproductions of TypeSafe Jev's Decision ModelRead the thread on Hacker NewsHacker News: Software Bombe and live Enigma demo where Jev ranks cribs and judges plaintext.
ag_212 pointsShow HN: Jev as the Bletchley Park Analyst: A Software Bombe on EnigmaRead the thread on Hacker NewsHacker News: Show HN for the decide CLI: brew install, file context, and multi-question shell scripts.
vsekhar2 pointsShow HN: Decide – Jev decisions in the shell, scripts, and agent skillsRead the thread on Hacker NewsHow entries are chosen
Every entry is reviewed against the same bar, whether it comes from TypeSafe or the community.
- Public and working — no paywalls, sign-up walls, or dead links.
- Concrete — it has to be about Jev and the System One API, not generic LLM content.
- Useful today — something a developer can learn from or use right now.
- Distinct — if a similar entry exists, the description has to say what makes this one different: a different runtime, a cheaper path, a measured result, or a narrower focus.
- Documented — repos state their license, and docs are in English or carry an English summary.
Spam, affiliate links, and thin AI-generated content are rejected. New entries are expected to ship with a deep dive: two to five highlights, caveats, a quickstart snippet where one helps, and a verification date. Star counts are refreshed when an entry is touched.
Common questions
Short answers about Jev and how this directory works.
What is the Jev AI model?
Jev is TypeSafe AI's first System One model — an AI model that takes a state plus typed questions and returns one structured answer per question: a choice, an ordered score, or the probability of yes, with calibrated confidence on Choice and Score answers. Code can branch on the result without parsing generated text.
What is the difference between Choice, Score, and Noul?
They are the three question primitives. Choice picks one of up to 255 options, Score rates on 2 to 10 ordered levels, and Noul answers yes or no. Choice and Score answers carry a confidence between 0 and 1; a Noul answer is the probability of yes.
Which Jev SDKs and client libraries are available?
TypeSafe maintains official JavaScript/TypeScript and Python SDKs plus a Python adapter. The community has built clients for Go, .NET, PHP, Laravel, Rust, Elixir, Ruby, and Scala, along with framework integrations such as DSPy, LlamaIndex, and Home Assistant. All of them are listed in the Repos & SDKs category.
Is the directory free, and is it affiliated with TypeSafe AI?
The directory is free to browse: every entry is reviewed, and spam, affiliate links, and sign-up walls are rejected. JevDirectory.org is community-run and not affiliated with TypeSafe AI — Jev and System One are TypeSafe AI trademarks.
How often is the directory updated?
Entries are added as new resources appear and re-checked when they are touched. The directory currently holds 3919 entries from 293 source sites, last updated Nov 6, 2026.
What is a verified deep dive?
Most entries include a deep dive: two to five highlight bullets someone would only know from using the resource, a caveats note about limits and requirements, an optional quickstart snippet, and the date the link was last checked. 3919 of the 3919 entries carry one today.
For the canonical details, read the official TypeSafe docs and the API reference.