Voice quality checks
Does the agent talk over the caller?
yes · no
Example: Hume Audio Decisions API docs · Source ↗
Decision models return structured answers, such as categories or scores. They can also return a yes/no probability. Jev returns these without generating text; some alternatives generate an option letter. People use them in games and websites, and in AI agents.
How it works
A message or an agent's history can be input. You can also send a game state or a web page.
For a Choice question, you supply the allowed options and receive probabilities for them.
Use the probabilities to decide which answers to act on and which to send to a person or a bigger model.
The odds above illustrate a Choice question. Jev, and the models built to its API, also take Noul questions, which return a yes/no probability; the name comes from the Bernoulli distribution. A Score question returns a value along your defined scale, which can fall between the numeric levels you supply. source.
A generative LLM can return structured output that your code reads. It generates its answer one token at a time. Jev returns structured answers without generating text. Together reports that Tev1 keeps a text-generation head and is trained to return one option letter. Simon Willison Together
TypeSafe calls Jev a System One model, after Daniel Kahneman's split between fast, intuitive thinking and slow, deliberate reasoning. Liquid AI uses "decision models" for d1. TypeSafe Liquid
The interface isn't. Scoring declared labels in one pass goes back years, from zero-shot classifiers to GLiNER. What is new is a hosted model sold for it, with calibrated probabilities as its stated training goal, at this price and speed. The lineage ↓
Use cases
The newest builds we've found. Each card links to its use case, with a source for the example.
Does the agent talk over the caller?
yes · no
Example: Hume Audio Decisions API docs · Source ↗
Does this vendor support SAML SSO and EU data residency?
yes · no
Example: Parallel Decisions API benchmark post · Source ↗
Does the answer to this question lie under this section?
yes · no
Has the conversation moved to a new topic?
same · subtopic · new_topic
Example: topic-gate · Source ↗
Is this note about a trip?
yes · no
Example: Laya Notes · Source ↗
Is music playing?
yes · no · cant_tell
Could executing this action cause data loss, a charge, a logout, an account or permission change, a message sent to another person, or another irreversible side effect?
yes · no
Does this output follow this rule from the skill?
yes · no
Example: skill-tester · Source ↗
How much reasoning effort does this task need?
low · medium · high
Example: jev-router for Claude Code · Source ↗
Should the bot reply now?
true · false
Example: JEV Damocles for AstrBot · Source ↗
Does this declaration implement the behaviour in the query?
yes · no
Does this claim from the call contradict the reported figures?
yes · no
Example: narrative-audit-jev · Source ↗
Is there room in this shot for one more person?
yes · no
Example: Cameo for ComfyUI · Source ↗
Which project does this file belong to?
Podcast · Client site · Home admin · None of these
Is the user trying to find, track or obtain private information about a specific person?
yes · no
Example: Exposure-aware routing · Source ↗
Does this folder contain only caches, temporary files, crash dumps or leftover downloads that apps recreate?
yes · no
Example: SpaceSniffer, Jev edition · Source ↗
Is the mixing step happening at this time?
Present · Absent · Unclear
Do the supplied slides include a pricing table?
true · false
Example: idec for local Ollama · Source ↗
Which team should handle this support ticket?
Billing · Technical · Account · Sales
Example: laya-pet ticket router · Source ↗
Is this reply interested?
yes · no
Example: Jev for Marketers guide · Source ↗
Is this issue from someone blocked from upgrading?
yes · no · unknown
Example: Better Backlog for GitHub issues · Source ↗
Which of the options the agent offered should it go with?
Option 1 · Option 2 · Option 3
Example: opencode-auto-question plugin · Source ↗
Does this request need the memory tools?
yes · no
Example: pi-jev-lazy-load for Pi · Source ↗
Which remediation path should resolve this failing custom setting?
Configure devices · Ask users · Fix policy · Review
Example: intune-why for Microsoft Intune · Source ↗
Does this listing match what I'm looking for?
yes · no
Example: Jev Content Filter for classified ads · Source ↗
Which element on this page does the step mean?
Checkout button · Cart icon · First name field · Continue button
Example: Decisium, plain-English UI tests · Source ↗
Which catalogue item is "5# limes"?
Limes, 5 lb · Limes, case · Lemons, 5 lb · none of these
Example: order-matcher for restaurant orders · Source ↗
How does this clause treat users in disputes?
not addressed · standard or protective · somewhat stricter · abusive
Example: Jev ToS Checker for Chrome · Source ↗
What caused this failed test?
environment or timeout · flaky assertion · genuine regression · API contract mismatch
Example: Jev CI triage · Source ↗
Does this news item carry PRC state framing?
yes · no
Example: West Philippine Sea Watch · Source ↗
Does this column hold personal data?
yes · no
Example: jev-pii, PII discovery in DuckDB · Source ↗
What kind of attention does this notification need?
action required · ephemeral interrupt · persistent status · review later · hide
Example: QuietFlow for Android · Source ↗
Does this existing repo already do the same job as the idea?
yes · no
Example: buildornot, a verdict on your side project · Source ↗
What does this villager do for the next two hours?
eat · rest · work · explore
Example: The Village, run on Jev · Source ↗
Can this question be answered from the textbook chapter?
yes · no
Example: Textbook Voice Tutor, Grade 2 · Source ↗
Which major group does this job ad belong to?
Professionals · Associate professionals and technicians · Clerical support workers · Service and sales workers
Example: SSOC 2024 Decision Trail · Source ↗
Has the other person answered this question yet?
yes · no
Example: Call Coach, a live interview checklist · Source ↗
Does this product's text say it contains chicken?
supported · contradicted · unknown · conflicting
Example: Petsona, evidence-backed pet product tags · Source ↗
Is this new paper about humanoid whole-body control?
yes · no
Example: arXiv Jev Digest, a daily screened reading list · Source ↗
Which tissue type is in this slide tile?
tumour · normal · inflamed · none of these
Example: PathJev, decisions on pathology slides · Source ↗
Should this agent's request reach the controller?
allow · cache · defer · deny
Example: Talos gate for AI agents on factory networks · Source ↗
Is this email for you?
for you · to review · junk
Example: Mail Triage, on-device with Laya · Source ↗
Does this post match my interests?
show · hide
Example: LinkedIn Focus Filter, in-browser with Kev · Source ↗
Does this record's text match the question?
yes · no
Example: Laya inside DuckDB · Source ↗
How big is this request?
question · small change · big change · too vague
Example: ClauJev, Jev checking Claude Code · Source ↗
Should the agent talk now?
speak · keep listening · yield · mm-hm
Example: TYGER full-duplex voice agent · Source ↗
Does this pipe pass inspection?
pass · defect · recheck
Example: OpenCV + Laya inspection workbench · Source ↗
Should this bash command run?
allow · block
Example: guardrails-md for coding agents · Source ↗
Will this happen by the deadline?
yes · no
Example: Three decision models vs Polymarket and Kalshi · Source ↗
What should the player do next?
left · right · jump · wait
Example: Mario, played from emulator state · Source ↗
Which element on this page gets clicked?
search box · results · next page
Example: browser-use: flights in 7.1 s · Source ↗
What's the next action on screen?
click · type · scroll · done
Example: typesafe-computer-use · Source ↗
How relevant is this result to the query?
a score on a scale
What do we do in this block?
buy · sell · hold
Example: jev-trader, one decision per block · Source ↗
Where does the drone go next?
forward · left · right · climb
Example: Camera-input drone in simulation · Source ↗
Is this commit a security fix?
yes · no
Example: commit-miner · Source ↗
Is this element clutter?
yes · no
Example: unclutter browser extension · Source ↗
Models
Hosted models, open weights you can run yourself, and the community projects around them. Tap any entry for what it is, what its maker claims, and where that claim comes from.
TypeSafe AI · 15 Sep 2026 · HOSTED
The model that named the category. A hosted API: send your app's state plus typed questions (Choice, Score or Noul) and get a probability back for each answer. It generates no text, and its weights are not public.
Source: Announcement → Source: Jev 1.14 → Source: Docs → Source: On OpenRouter →
OpenAI · 29 Sep 2026 · BETA
OpenAI's decision endpoint, announced at DevDay 2026. It focuses GPT-6 Luna on a fixed set of developer-defined answers, to classify inputs, route requests or choose an agent's next action.
Source: API changelog → Source: DevDay announcement → Source: DevDay recap →
Liquid AI · 29 Sep 2026 · HOSTED
Liquid AI's decision model family. The hosted d1 takes the same three question types as Jev (Noul, Choice and Score). On 7 Oct Liquid released two small open siblings you can run on a device, and on 15 Oct a bigger hosted one, d1-ultra, joined OpenRouter. On 27 Oct d1.1, the successor to d1, joined OpenRouter at the same price, with -latest aliases that follow each family's newest version. On 27 Oct Liquid announced two more open models, d1-2B (text, pictures and video) and d1-Omni-2B (adds audio), built on LFM3; their Hugging Face repositories went up on 24 Oct.
Source: Docs → Source: Announcement → Source: Decision Index → Source: Open d1 release → Source: d1.1 on OpenRouter → Source: d1-2B →
Inception · 30 Sep 2026 · HOSTED
Inception's decision model, served as a System One endpoint: send state and typed questions, get a choice, a score or a yes/no answer, each with a probability.
Upstage · 28 Sep 2026 · HOSTED
Upstage's decision endpoint, available through OpenRouter. The date is when OpenRouter announced it; we have not found an announcement from Upstage itself. A faster sibling, Solar Decide Flash, joined on 8 Oct 2026 with a 524K context.
Source: OpenRouter → Source: OpenRouter announcement → Source: Solar Decide Flash →
Respan · 24 Sep 2026 · HOSTED
A one-pass classifier for agent traces. For each behaviour you define, it returns the probability that it is present, absent or not observable, without generating text. Its successor, Span-02 (15 Oct 2026), reads images too, can return a number directly, and ships as Apache-2.0 open weights.
meraGPT · HOSTED
A hosted decision model served only at /v1/systemone, the request format TypeSafe's SDK speaks. Its site doesn't say who runs it or when it launched.
Microsoft · 9 Oct 2026 · HOSTED
Microsoft's decision-scoring model for routing, classification, prioritisation, verification and workflow control. Post-trained from Qwen3.5-9B, available in Microsoft Foundry and on OpenRouter.
Cloudflare · 9 Oct 2026 · HOSTED
The Clef family on Workers AI answers typed questions in one pass. Clef-omni adds audio and video, so it can decide about what is seen and heard at the same time. The same update cut Clef-flash to $0.038 per million input tokens.
Snowflake · 8 Oct 2026 · PREVIEW
A decision model you call from SQL. It classifies, scores, filters and routes rows inside Snowflake through AI_COMPLETE. A lower-latency Decision Fast, with REST access, followed on 19 Oct.
Celeris · 8 Oct 2026 · HOSTED
A closed decision model for text, structured data and images that can give a one-sentence reason alongside each probability.
Subquadratic · 12 Oct 2026 · PREVIEW
A long-context decision model: each decision can read up to 384K tokens of text and images, aimed at agent workflows.
ScaleDown · 13 Oct 2026 · HOSTED
Scores text or a whole document against your own choice, yes/no and ordered-scale questions. Request-compatible with Jev's Decisions API. The date is OpenRouter's listing.
Cloudflare · 19 Oct 2026 · PREVIEW
A new branch of the family: a System One extraction model. Where a decision model picks from answers you list, Clairic extracts, computes, estimates or forecasts values from unstructured input and returns them as typed answers: an invoice total, a delivery date, a repair cost, a 24-hour traffic forecast. It does this quickly, without an LLM writing prose.
Parallel · 25 Oct 2026 · HOSTED
A decision engine that searches the live web before it answers. You send typed questions about a company, product or policy, and one call returns typed answers with probabilities (or a refusal) plus a shared list of the web pages it used, so it can answer things a model only knows from training data cannot, such as whether a company has an open job right now.
Source: Announcement → Source: Docs → Source: Benchmark → Source: Benchmark announcement →
Jared Palmer · 1 Oct 2026 · OPEN
Kev is Jared Palmer's family of open, Jev-compatible decision models, from 0.8B to 27B parameters. A pointer head on a Qwen base scores each option, and the API matches TypeSafe's, so their SDK works against a local Kev server. Kev 1.0, released 1 Oct 2026, pins the family's checkpoints, model cards, evaluation suites and serving code; it introduces no newly trained weights.
Source: GitHub → Source: Kev 1.0 release → Source: Hugging Face → Source: Kev 4B on OpenRouter → Source: GitHub API: stars →
DecisionTune · 4 Oct 2026 · OPEN
A local decision model built on ModernBERT-large: give it a state, a question and options, and it returns a probability for each option in one encoder pass. It handles choice and yes/no questions without generating text, through PyTorch, MLX or ONNX. The open weights are under Apache-2.0. The date is when the weights repository went up on Hugging Face.
Together AI · 23 Sep 2026 · OPEN
Together's Jev-inspired experiment: a Qwen3.5-4B fine-tune that reads a state, a question and 2 to 24 lettered options. Together says plainly it is not a non-autoregressive runtime like Jev: it keeps a standard next-token head and generates its answer as one letter.
Source: Hugging Face → Source: GitHub → Source: Announcement → Source: Untuned 4B baseline test →
Fastino · 24 Sep 2026 · OPEN
A 340M-parameter encoder decision model from the team behind GLiNER, fine-tuned from GLiNER2. It scores the allowed answers to typed questions in one pass and is small enough to run on a CPU.
Convai Innovations · 18 Sep 2026 · OPEN
A non-autoregressive decision engine on ModernBERT-large (421M), with a 322M multilingual sibling. Its author, Nandakishor Mukkunnoth, says he built the same approach a year before Jev launched.
Bespoke Labs · 18 Sep 2026 · OPEN
A 9B decision model, LoRA-tuned from Qwen3.5-9B, with its 2,676 training examples published. One of the first two models in Ollama's decision support. Bespoke Labs followed with hosted Nimble v4 (9B) and v4-max (27B) on 13 Oct 2026.
Source: GitHub → Source: Ollama → Source: Hugging Face → Source: Nimble README →
vLLM Semantic Router · 22 Sep 2026 · OPEN
Open decision models from the vLLM Semantic Router project, published as "Decision 1.0: towards open decision foundation models". Six were announced on 22 Sep. The follow-up, Decision 3.0 (d3), is Apache-2.0 and reads text, images and video, with sizes up to 27B.
Source: Hugging Face → Source: Announcement PR → Source: d3 on Hugging Face →
Perplexity · 6 Oct 2026 · OPEN
Perplexity's decision model reads text and images and returns a probability for every answer. It ships as Apache-2.0 open weights and as a hosted API. V1.1 (27B) halved the price on 6 Oct; V1.2 and a 4B Decider Flash followed on 20 Oct.
Source: V1.2 announcement → Source: Hugging Face → Source: V1.1 announcement →
Nace.AI · 6 Oct 2026 · OPEN
Drex 2.0 returns probabilities over typed answers for text, with open weights under Nace.AI's Open RAIL licence. Drex Max (13 Oct) adds images and screenshots, hosted only. The date is Drex 2.0's release in Nace.AI's model table.
H2O.ai · 7 Oct 2026 · OPEN
frontier-infra · 10 Oct 2026 · OPEN
Open decision models that read option-label probabilities in a single pass with thinking switched off: you get a pick and a confidence, not an essay. GGUF and MLX builds sit next to the full weights. Released 10 Oct 2026, written up on 16 Oct.
FrontiersMind · 14 Oct 2026 · OPEN
A 27B open decision model that answers in English, Hindi, Tamil and Kannada. The makers say Jev still leads on some reasoning benchmarks.
IBM · 15 Oct 2026 · OPEN
IBM's switchable models do chat, decisions and speech-to-text from one endpoint. 4.1 adds a general-purpose Decision adapter that speaks the /v1/systemone interface.
NineNineSix · 17 Oct 2026 · OPEN
A decision model for speech. It listens to the microphone and your text-to-speech at the same time and keeps deciding whether to speak, keep listening, yield or drop in an "mm-hm", scoring audio in 80 ms frames streamed in 320 ms hops. Built for full-duplex voice agents, with an open-source agent (tyger-s2s) on top.
Source: Hugging Face → Source: Announcement → Source: Voice agent →
Tractian · 22 Oct 2026 · OPEN
A decision model that stands between AI agents and the controllers that run factories. Every Modbus or OPC UA request goes through one gate, where a small model decides in a single pass to allow it, answer from cache, defer it or deny it, and every decision is logged. The date is when the repo and weights went up.
Baseten (Base Labs) · 21 Oct 2026 · OPEN
Two open decision models from Base Labs, Baseten's research team: mammoth-1 (27B) and elephant-1 (9B), post-trained from Qwen3.5. They read text and images and return a probability for every option of each question, with all questions sharing one prefill of the state. Baseten launched them claiming the top spot on the community Jev Decision Index. On 29 Oct a third, smaller model joined: joey-1 (2B, built on Qwen3.5-2B), pitched as the fastest and cheapest.
Source: Announcement → Source: Technical report → Source: mammoth-1 on Hugging Face → Source: elephant-1 on Hugging Face → Source: joey-1 →
labqoat · 20 Oct 2026 · OPEN
A tiny open decision model that runs locally, built on Qwen3.5-0.8B and reading text and images. The date is when the weights went up on Hugging Face.
decido-ai · 22 Oct 2026 · OPEN
An open decision model built to run on a laptop CPU: a LoRA fine-tune of Gemma 4 26B-A4B shipped as an 8-bit GGUF for llama.cpp, answering in the same wire format as Jev's System One API. The date is when the weights went up.
Arc53 (DocsGPT) · 11 Oct 2026 · OPEN
The small decision model inside DocsGPT, used for quick judgements such as whether a message is a prompt injection or whether a chunk answers the question. It runs on CPU with ONNX Runtime. The date is when v0.2 went up on Hugging Face.
Valen Team · 5 Oct 2026 · OPEN
Open decision models in three sizes that read text, images and video, built on Qwen3.5. They answer through both Jev's System One format and OpenAI's Decisions format, with a free web demo and API. The date is when the first weights went up on Hugging Face; the 4B followed on 6 October.
Sivasuthan · 24 Oct 2026 · OPEN
An open decision model for images: give it a photo, document, receipt, chart or screenshot plus a typed question and it returns a probability for every allowed answer, served over Jev's System One API. The date is when the weights went up.
egma-ai · 27 Oct 2026 · OPEN
An open decision model that listens instead of reading: give it an audio clip and a typed question (how does the speaker feel? does the pitch rise?) and it returns a probability for every option, without a transcript. It is Qwen3-Omni-30B-A3B with frozen weights and a decision head in place of the text output. The date is when the weights went up on Hugging Face.
Crele · 30 Oct 2026 · OPEN
A decision model for live audio. You ask yes/no questions in plain English (is music playing? is someone speaking?) and it answers every 40 ms of a stream with yes, no or can't tell, real time on a laptop CPU. The date is when the weights went up on Hugging Face.
Fastino · 19 Oct 2026 · OPEN
Fastino's big decision model, built on Qwen3.8-27B: give it text, JSON or images plus typed questions and it returns a probability for every option, with nothing generated. The weights are on Hugging Face under Fastino's own community licence, and Fastino also hosts it as an API. The date is when the weights repository went up; the version scored on the index is 1.2.
Source: Hugging Face → Source: API docs → Source: Decision Index →
Daniel van Strien · 28 Oct 2026 · OPEN
A 380M-parameter decision model for data curation: give it a web page, prompt, maths problem or library record and typed questions (topic, format, educational value, difficulty) and it returns a probability for every option. The release centres on its recipe: the data-building and training scripts are public, with source and labeller/licence metadata where known, and 516,964 of the 535,730 training rows are released (web text omitted).
PelaAI · 7 Oct 2026 · OPEN
A fast-moving family of open decision models from PelaAI, trained in an agent-driven data loop, with English and Chinese in mind: from 0.8B up to 35B-A3B, with new generations posted every few days. The newest big one, KnowLine-31B-Gen1 on a Gemma 4 base, went up on 23 Oct. The date is the first family release on Hugging Face (4B-Gen1).
Ollama · 28 Sep 2026 · RUNTIME
TheoLeeCJ · COMMUNITY
Reproduces Jev's interface pattern with open models: it reads typed option probabilities directly from a model, with no answer sentence and no decoding loop. Runs in your browser, or at home on a GPU, Apple Silicon or CPU. Independent of TypeSafe, and it does not reproduce Jev's model or training.
bnsd55 · COMMUNITY
Parallel constrained decisions for local models through mlx-lm on Apple Silicon: the schema is a set of typed fields, the context is prefilled once and broadcast across one batch row per field. MIT licensed. Large answer sets may be chunked rather than done in a single pass.
kw2828 · COMMUNITY
A browser and local playground for decision experiments on memory, uncertainty and control, using Qwen-family models and returning option probabilities. MIT licensed. Its README states it is independent of Jev, of openjev.com and of zhihz/openjev.
akash-kamat · COMMUNITY
Gemma 3 270M with a scoring head, producing a decision in a single forward pass with no text generation. Code and adapter weights are publicly readable, but the repository carries no licence, so it is not open source in the legal sense. The Gemma base weights remain under their own separate terms.
leofmarciano · COMMUNITY
Groot 1 S, a 149M-parameter open typed-decision model that speaks the System One request format and reads states of up to 64k tokens in the maker's tests. Its forward pass also ships in the Bend language.
jwuthri-cloud · COMMUNITY
A self-hosted decision model with a Jev-compatible API: you run the server on your own GPU, and a 230 MB vision adapter on Qwen3.5-4B reads text and images. Python SDK, server and fine-tuning in one package.
prismhq · TOOLING
A LiteLLM-based router with an optional Jev decision backend: when a TypeSafe API key is configured it can use Jev to pick a model per request, and otherwise falls back to its own rules-based decider. MIT licensed.
mizorewww · TOOLING
Runs the open Laya decision models natively on Apple Silicon with MLX: no PyTorch, no cloud API, zero output tokens. Its demo plays Snake, calling Laya for every move with a safety layer that can override unsafe ones. Apache-2.0, and more starred than any project on our list except browser-use's jev-ultrafast.
Hume AI · 2 Nov 2026 · HOSTED
Hume's hosted model answers questions about speech and sound with probabilities. Send one MP3 or WAV recording, 5 seconds to 5 minutes long, with questions in plain language. It supports yes/no, choice, score and time-span answers, and returns no transcript. Hume says the probabilities estimate how human raters would respond.
Source: Announcement → Source: Product and playground → Source: Docs → Source: Billing → Source: Audio preparation → Source: Launch changelog → Source: Python SDK →
Zoom · 19 Oct 2026 · HOSTED
Zoom's hosted Decisions API and Decisions API Pro accept text, images or both. Define several questions about the same input and get structured answers in one call for condition checks, option selection and scoring.
Source: Product, playground and pricing → Source: Launch changelog →
Ant Digital Technologies (蚂蚁数科) · 22 Oct 2026 · HOSTED
Ant Digital Technologies' System One model for choosing answers to business questions, including native multi-select and decisions ordered by dependencies. IT之家 reported its 22 Oct 2026 launch in 4B and 27B versions for customer service, risk and finance. Ant's current DTMaaS listing describes the lingdt-decision-1.0 endpoint as a 4B test model.
Source: DTMaaS model page → Source: Official model data and API example → Source: API guide → Source: Launch report (IT之家) → Source: Jev pricing source → Source: Microsoft-Decision-1 source →
besktech (beskvladimir-create) · 1 Nov 2026 · OPEN
A Russian-first typed-decision model built on mmBERT-base (307M), with a separate scoring head. It runs on CPU and supports yes/no, choice and ordinal scales. The maker says it is unaffiliated with TypeSafe AI and was not trained on Jev outputs. Hugging Face now documents v1.1; the GitHub README still reports v1 results.
Source: Weights and v1.1 model card → Source: GitHub → Source: Release metadata →
Claims are marked vendor-reported until someone independent reproduces them. Inclusion is not endorsement, and nobody pays to be listed. Something missing or wrong? if@decisionmodels.ai
Projects
Each one opened and checked against its own repo, with star counts as of the day each was added. Jev, released on 15 September 2026, is behind 77 of the 114 projects; the other 37 run on twenty-two different models, or models you load locally. Checked 03 Nov 2026, GitHub search returned 11,206 public repositories matching "jev" created from 15 to 30 September, and 27 created from 11 to 14 September.
Zurich to London on Google Flights in 7.1 seconds, including model calls, generated text, browser work and loading waits. Swapping to a decision model took median task time from 9.450s to 7.092s and median browser protocol calls from 1,092 to 101.
One natural-language goal, on the real web, at 1x speed. Maker-reported: three repeats of one task on one browser profile.
Figures from the project's own README and docs/performance.md. The maintainers note this is three repeats of one task on one browser profile, not a general benchmark.
Attempts one trade decision per Monad block on the Kuru MON-USDC market, and holds when the model misses the block deadline.
A pixel-art racer where d1 picks left, centre or right on every tick, with a mode that races it against Jev. Liquid AI's own example, in their cookbook repo, so the star count is for the whole repo.
OCRs the screen, then classifies the next action. The claimed per-step cost is the maintainer's own figure, not independently measured.
A staged review workflow with a local dashboard: each stage is a bounded judgement rather than a written critique.
An agent that plays Super Mario Bros. by classifying the next action from structured emulator state rather than generating a plan.
A local agent workflow tool written in C. Its recap command sends each session transcript to Solar Decide, which labels every statement as a goal, decision, constraint, done or noise before a chat model writes the summary.
A browser extension that decides, element by element, what on a page is clutter, with reusable template rules.
A drone experiment in MuJoCo with a small judgement model in the control loop, on camera input. The maintainer calls the harder tunnel run a partial result.
An LLM plans, reads and writes; Jev picks each action from the controls indexed on the page, and code stops irreversible clicks without authorization. On 46 tasks run three times each against the hosted Browser Use agent, it passed 99.2% of runs to 96.3% and cost $0.0296 a run to $0.3180, 10.7x less, but was slower: 18.9 s a task to 15.3 s (published campaign release-0.7.1).
Scores every sentence of a video and renders the running result as an overlay.
A paper-trading research app where Jev makes the decisions, wrapped in an animated 3D command center with model pets and a ten-timeframe research panel. Every trade is simulated; there is no real order path.
A local inspection workbench: OpenCV measures the image, hard rules decide anything numeric, and a Laya decision model cross-checks the result. Built for fruit picking, weed and pest monitoring, and steel pipe and textile checks. Version 3.0 also trains your own Laya-format decision models.
A browser voice agent where the TYGER decision model is the ears: it transcribes you and decides when you've finished, when you've cut in, and when to say "mm-hm", while an LLM writes the replies.
QA for Remotion videos: code measures every frame, the Sage decision model answers yes/no questions about each one, and only the frames it flags go on to the slower critics.
Classifies git diffs and messages into bug fixes, security fixes with CWEs, and change types.
Checks coding-agent tool calls with rules and a local multilingual Laya fine-tune, returning allow, review or block. VENDOR-REPORTED, base to fine-tune: the model alone held 32 then 39 of 43 attacks and allowed 25 then 44 of 50 everyday commands. Six end-to-end verdicts improved and two worsened, missing a preset no-regression criterion. Accuracy on 162 held-out training rows rose from 0.611 to 0.852. The author inspected the benchmark and evaluation sets between training runs, so they are less independent than a fresh test.
Routes lead, contact and issue forms with Jev through Vercel AI Gateway and the AI SDK. It accepts Jev's destination when confidence is at least 95%; otherwise, or if Jev fails, openai/gpt-6-luna-fast evaluates the submission independently. The result shows the destination, deciding model, timings and an email preview, with optional delivery.
A live site where OpenAI Decisions, Jev and Cloudflare's Clef put a probability on live questions after reading recent news, never the odds. Their probabilities are compared against Polymarket and Kalshi prices.
Decision models forget everything after each answer; Nouli keeps a session. Type facts as you learn them and start a line with ? to ask a new question; earlier facts and answers stay in the history. A Rust CLI and library that can chain sessions into decision graphs, with Jev through its TypeSafe adapter or other providers.
A small decision model scores every bash command against your GUARDRAILS.md in about 100 ms (maker's figure), before an opencode, pi or Claude Code agent runs it. Commands scoring above the threshold for data loss, secret leaks or rule breaks are blocked; the README warns it can still let risky ones through.
A Claude Code plugin that asks Jev short typed questions while Claude works: how big is this request, is this command destructive, was the work really done. It suggests an effort level per request, and in enforce mode a confident check can block a "done!" when the work looks incomplete.
A native DuckDB extension that adds seven Jev-style SQL functions backed by a local Laya model: DuckDB handles geometry and distance, Laya reads the text fields. It uses no hosted model or API keys.
Runs Bespoke Nimble-9B and three Laya checkpoints as players over the same 200 seeds of a headless Slay the Spire simulator, zero-shot and fine-tuned. The results are the authors' own.
A drop-in React inspector that turns an app's Jev requests into interactive diagrams of the context, each question and the returned answers. Marked experimental.
A local web page and CLI that reads a repo's GitHub issues and lets you filter them by typing a criterion, such as people blocked from upgrading. Each criterion is asked of a decision model through your TypeSafe key or a gateway; add another to narrow the list.
A local app you point at a messy folder like Downloads: it works out which of your projects each loose file belongs to, reading the README or CLAUDE.md in your code folders, and files it into a matching folder under your chosen output location. Nothing moves until you press Move, and one click puts it all back.
Benchmark code and a 362-question test set that sends each item to Jev and to Kev 4B, and reports accuracy, calibration, latency and cost.
Tests Span-01 Lite as a second check on whether narration text is in the right language, after a regex pass, against a general chat model. The author notes the 23-case test set is small.
Classifies Apple Mail messages as for you, to review or junk with a Laya model running on your own computer, and shows a report of what it would do. Mail never goes to an external AI service, and it never deletes anything.
A Chrome extension that hides LinkedIn posts that don't match your stated interests, using Kev 0.6B running in the browser on WebGPU. Post text never leaves the browser.
A decision model for histopathology: given a tissue image, a question and candidate answers chosen at run time, including 'none of these', it returns a probability for each in one forward pass. Built on MedGemma-1.5-4B with LoRA adapters and a decision head, with code to rebuild every number in the authors' preprint.
Runs an open System One model locally on Apple silicon against ten medical benchmarks, from USMLE-style questions to emergency-department admission and 30-day readmission, and compares it with zero-shot LLMs and supervised baselines.
A DeepSeek Harness plugin where Jev, via OpenRouter, judges each message as simple, standard, complex or escalated and raises or lowers only the reasoning effort of the model you picked. If Jev fails, it switches itself off.
Each ant is a Jev agent that sees the cells around it and its neighbours' headings and decides which way to move and whether to settle. A thousand small decisions turn a random scatter into a word or an uploaded picture; the maker puts a word at about fifteen seconds and seven cents.
A Chrome extension that hides spam and noise in X posts and replies by combining Jev's probabilities for rules you write in plain language with local signals, and decides locally what to hide. Everything hidden can be restored in place, and if the API fails, posts are shown.
Write test steps as plain-English Gherkin and Jev picks the element, action and check for each one from options Decisium reads off the page; code, not the model, decides whether a step passed. Resolved steps are recorded so CI can replay them with no model calls.
A desktop app that finds and downloads decision-model GGUFs from Hugging Face, runs them on llama.cpp, and serves a local API with choice, yes/no and score questions, plus a playground for trying presets.
A Claude Code Stop hook with a local fine-tune of DecisionTune 1.0 that can block a turn ending that hands work back to the user. VENDOR-REPORTED on 864 test turn ends: the moderate setting caught 67.5% (195 of 289) of hand-backs at 15.8% false interventions, against 56.7% and 14.6% for phrase rules. That test split guided development. Both members were correct in only 16% (37 of 229) of synthetic pairs; the model did not beat phrase rules there. Warm model latency was a median 27 ms on an M5 Pro. The hook cannot verify claimed task completion.
A macOS app whose browser agent makes every choice, such as which element to click, as one classification by Nimble-9B running locally in Ollama. Low-confidence calls go to the person.
Routes support messages through regex, then a small classifier, then decision models, then an LLM as a last resort, all running locally. Laya answers the guardrail and department questions, and Tev1 is the default for the next tier.
A one-file browser Pacman where the open directions at each step become the options in a single question to a local Tev1 model through Ollama's decision endpoint.
A proof of concept that classifies incoming requests with the Decisions API preview and hands them to worker agents. Its README warns that the preview's request format is not yet published.
A TypeScript library where Jev picks the table, columns, filter, order and limit from what the schema offers, so it can choose a column but never write a query. Drizzle, Prisma or a SQL builder turns the plan into a real, parameterised query.
A 3D boxing game where Jev, GPT-4o-mini and Claude Haiku pick each move from the same state and legal actions, and a seeded rules engine resolves the hits. The maker calls the 56-bout results a pilot, not a ranking.
Tags each pet product's species, life stage and allergens from the store's own text, asking Jev or Laya one typed question per value: supported, contradicted, unknown or conflicting, with the source sentence attached. Merchants can approve, correct and lock tags, or let a validated policy accept them automatically. A hackathon entry for AIBoomi Startup Weekend Kochi, 23-24 Oct.
A GitHub Actions job where Jev scores each new paper from your chosen arXiv categories against each of your research interests, code ranks them and keeps the top few, and only those go to DeepSeek for a summary emailed to you. The default follows humanoid robotics papers.
Ask a question and Jev, via Vercel AI Gateway, gives each of 100 well-known American politicians a yes/no probability; the faces above the threshold float up out of a physics pile.
A macOS app that transcribes both sides of a call on your Mac with Whisper while Jev ticks off each of your questions once it has been answered, notes which question you just asked, and flags surprising answers for a follow-up. The maker puts Jev's cost at about 1 to 2 US cents for a 45-minute interview.
Answers a seven-year-old's spoken questions from one scanned Grade 2 textbook chapter. Jev decides whether each new question can be answered from the chapter and, with the optional verifier on, whether the drafted answer's claims are supported by the retrieved passages; common questions are matched to cached answers. On the maker's 14-question test, the decisions API cut average answer time from 4.1 to 1.7 seconds and the cost of each gate call about 25 times, against the chat wrapper.
Paste a job ad and watch Jev walk Singapore's 2024 occupation classification level by level, keeping the three best paths at each step, to a five-digit code, drawn as an animated pixel-art crowd. Six requests, about 1.4 seconds, per the README.
Speak several instructions in one run-on sentence and Jev picks each action from a menu of skills, and any app from those installed on your Mac, while you are still talking. Anything outside the menu goes to Claude for a proposed shell command, which waits for your approval unless you turn on trust mode.
A Thunderbird extension that tags your email with decision models served by Ollama's /v1/systemone endpoint, such as Tev1 0.8B, Tev1 4B or Nimble, and keeps learning from the tags you add by hand.
An MCP server that gives an AI assistant yes/no, choice and score tools backed by whichever decision model you configure: any /v1/systemone endpoint such as Jev or Clef, or Laya running on Apple silicon. Its tools never name a model family.
A pixel-art village in the browser where Jev, via OpenRouter, chooses what each adult does for the next two hours, while fixed rules handle urgent needs, night-time sleep and children. The maker built it to test whether Jev's choices stay sensible and reflect each villager's personality.
Describe a side project and get BUILD, MAYBE or DON'T BUILD with the reasons: Jev rates pain, reach and feasibility out of five and judges whether the GitHub and Hacker News results it finds already do the job. A run costs well under a cent.
A DuckDB function that asks Jev typed questions about each database column: is it personal data, which kind, and whether GDPR, CCPA-CPRA, HIPAA, LGPD, PIPEDA or DPDP apply. Whether to keep, mask, hash or drop it is decided by code, not the model, and a failed call sends the column to review. Also runs in the browser.
Grades search results with Jev on a four-level scale, checked first against Amazon's human-graded shopping queries, and stores runs, results and judgments in DuckDB with the metrics computed in SQL. The author recommends checking it against at least 300 of your own labelled pairs before trusting it on another domain.
A Hugging Face app built on the R package decisions: upload a set of images or texts, ask Jev one question about each, and get every answer with its probabilities plus the R code to repeat the run. Visitors need no key or account; runs are checked against estimated session and daily budgets.
An Android app that can use Jev to sort notifications into five classes, from action required to hide, alongside local safety rules and corrections it learns from you. It starts in observation mode and never cancels notifications there; Jev is opt-in with your own key.
A public monitor of the West Philippine Sea with live ship positions, Sentinel-1 radar passes and Global Fishing Watch events. Jev answers four questions about each item from 13 news feeds, and two of them, whether it serves a Philippine reader and whether it carries PRC state framing, decide whether it is shown or held back. Live at wps.gdgt.link.
Jev, SAP-RPT-1, LLMs and a 15-line heuristic play Tetris on the same pieces and candidate moves, with live cost tracking. In the maker's 50-piece race Jev and the heuristic both cleared 19 lines and the others 18, a tie within noise; the difference was speed and cost.
Calls Jev, OpenAI's Decisions API, Upstage Solar Decide and local Ollama models through one interface and logs version, latency, tokens and cost. On picking the right one of 12 tools for 40 Korean and English requests, the maker measured Jev 1.13 at 40 of 40 and OpenAI Decisions at 0.90, against 0.85 for a generative model choosing directly.
A Chrome extension that splits the terms of service on the page into clauses and has Jev check each for eight kinds of one-sided term, from selling your data to forced arbitration, marking only clauses that go well beyond what major services do and showing each next to the original text. The maker says it is not legal advice.
Jev reads each failed test and decides the cause: environment or timeout, flaky assertion, genuine regression or API contract mismatch. Code re-runs the build only when every failure would be fixed by re-running (three attempts in all by default), gives flaky tests a low-priority ticket as well, and files tickets for real failures with the test's owner.
A Go command-line tool and MCP server that gives coding agents and shell scripts Jev's yes/no, choice, score and multi-question calls, with offline request previews and batch runs.
Visitors type what they need in their own words and Jev, via OpenRouter, picks the one page on the site that fits, choosing only from your list of pages. With no key or a slow reply it falls back to word matching.
Matches messy restaurant orders such as "2 cs portobellos, 5# limes" to exact catalogue items with Jev and flags uncertain lines for a person to check. The maker reports 96% exact matches on held-out test orders.
Sorts incoming mail for the maddy mail server into IMAP folders: Jev answers questions such as whether a message is spam or phishing, and rules in a config file turn the answers into a folder. It also learns from where you have filed mail before.
Reads Intune compliance data from Microsoft Graph, groups failing devices by the setting that fails, and ranks fixes by how many devices each makes fully compliant. For custom settings its rules don't cover, Jev can optionally pick the remediation path. You can try a live demo with a sample tenant.
A browser extension that asks Jev about every ad on the Bazoš classifieds site against criteria you write in plain language, and collapses the ones that don't match. Other marketplaces and auction sites are planned.
A small command-line tool that asks Jev to flip a coin N times and reports the share of heads.
Polls new Hacker News stories every two minutes, runs each unprocessed one through decision trees of Jev questions you define, and sends the stories that match any tree to a Telegram chat or channel.
An opencode plugin for autonomous runs: when the agent stops to ask the human a question, Jev (or an LLM, if you set one; the default tries Jev first) picks from the offered options. Below a configurable confidence (0.6 by default) it gathers context in a side session and decides again; below 0.35 the human answers after all.
A Pi extension that keeps most tool and skill descriptions out of the first request and loads them on demand. In its optional Jev mode, Jev decides before each prompt which ones the request needs.
An open research essay on three competitive 2026 US Senate races: compare stored Jev forecasts across different sets of evidence and different orderings of candidates and sources, with the exact requests and saved responses to inspect. The author calls it an experiment, not a calibrated forecast.
A Claude Code plugin that checks an agent's tool calls before they run, using hosted Jev when you configure a key and offline rules otherwise. In enforce mode it refuses a call with a reason; advisory mode only reports the verdict.
A free guide, a Claude skill and four editable recipes for typed marketing-ops decisions: route this lead, tag this asset, is this reply interested. Jev makes the call; an LLM still writes the copy.
A Chrome extension: describe what you don't want to see in plain words, and eligible posts on your X timeline get a probability per rule. By default, anything over your threshold folds into a one-line bar you can open again.
A small ticket router: with the Laya backend selected, each support ticket goes to Laya, the open Jev-style model, with three questions (which team, how urgent, is it spam). The default backend is a keyword stub, with a slot left for Jev.
A small demo with a live site: type a US or Indian stock and two Parallel Decisions calls, through Parallel's free MCP endpoint, assess its technicals and trading activity from the live web and return probabilities and sources.
Keeps the 50 busiest Polymarket questions forecast by several models, including Jev, Perplexity's Decider and an OpenAI model, all given filtered web search results but never the price. When markets resolve, each is scored on a leaderboard by Brier score, log loss and accuracy.
Give Jev a mission and some links: it gathers every item, judges each one live with counters and a running API bill on screen, and ends with a results page you can sort, spot-check and export. Runs on your own machine with your own keys.
A script that asks true/false and multiple-choice questions about an image, PDF, Word file, slide deck, spreadsheet or text, using a decision model on local Ollama. Validated with d1:3b and Clef-Flash; d1:3b is the default.
A Windows disk-space map that draws your drive as a 3D city and asks Jev which folders look safe to delete; those get a neon outline and land in a Reclaimable list, biggest first.
A step-by-step guide for people who don't write code: set up Jev with Claude Code and build a read-only assistant that ranks the last two weeks of Gmail into piles like Reply needed, Waiting on them and Possible phishing.
Simulates a moderation queue hit by a wave of privacy-invading posts the existing router did not prioritise: Claude reads 20 of those posts and 20 ordinary ones and drafts one yes/no question, and a fine-tuned version of the open decision model Laya asks it of every new post so the matches jump to the urgent queue.
Nace's DuckDB extension asks typed questions about rows from SQL and returns probabilities as column values, for routing support tickets, classifying documents or scoring data you already query. Nace also publishes MIT-licensed connectors for Snowflake, PostgreSQL, ClickHouse and Apache Spark, including Databricks SQL. Text and JSON calls default to drex-v2.0; images need an image-capable model such as drex-max.
Source: Snowflake → Source: PostgreSQL → Source: ClickHouse → Source: Apache Spark and Databricks SQL →
A ComfyUI workflow that adds you to an existing video. Liquid's d1-2B judges every shot (framing, whether there's room for one more person, where everyone stands) and casts the best ones, then image and video models put you in beside the people already there.
Written at Kariyer.net R&D to pull seniority, department and work type out of job ads. On 600 real ads with 7 questions each (4,200 decisions), Jev 1.13.0 scored 71.5% against 64.6% for the open tau-4b and 65.7% for a prompted 4B LLM. tau-4b was better calibrated (ECE 0.093 against 0.156), and it scored 95.7% on its own training data, which is why the author warns against benchmarking a model on the set it shipped with.
A harness that checks claims from earnings-call transcripts and footnotes against tables in DuckDB, on 64 synthetic cases. Perplexity's pplx-decider-flash-v1.2 scored 85.94% at about 1.4 s a case; a rule baseline scored 68.75% but caught only 2 of 9 Q&A deflections, which the decider caught 9 of 9. A chat model led on accuracy at 90.62%, with confidences stacked at 0.99 to 1.00. OpenRouter's chat endpoint rejected the decider with HTTP 400, so it runs through the separate /api/alpha/decisions endpoint.
jevgrep finds code by asking Jev thousands of yes/no questions. layagrep records those questions on SWE-bench tasks and fine-tunes the open Laya to answer them locally, with no API key and no code leaving the machine. A smoke run on 400 held-out questions lifted file-navigation AUC from 0.79 to 0.94 (Jev 0.97), but declaration relevance stayed at 0.58 (Jev 1.00).
Reads the probabilities of the answer tokens from one forward pass of an ordinary local LLM, with no fine-tuning, and serves them through a CLI, batch mode and a Jev-like API on Apple Silicon. On the 231 public JevBench questions, gemma-4-26b-a4b scored 84.8% at about 170 ms a question and ling-3.0-tiny 69.7% at about 45 ms; most of the misses are on the hard set.
Lets any agent operate Windows desktop apps without taking over the screen. A local 0.33B decision model, S1-Fast, reads the app's accessibility element table and does the sure steps. Uncertain steps go back to the agent or to a fine-tuned OneJev-4B, and irreversible ones stop until a person confirms them. It reports 68.4% macro step success on five public held-out test sets and 40 of 40 on its own development tasks.
A lab notebook for speeding up one decision model, H2O Lightning 4B, served through llama.cpp on an RTX 3060 Ti. Each request takes 2.13 to 2.45 times its roofline floor. The first measured change, a llama.cpp pull request, cut latency by 1.94% with bit-for-bit identical answers; the bigger gains in its list of 15 targets are labelled predictions.
An AstrBot plugin that decides whether a bot in a group chat should reply. It merges bursts of messages, separates conversation threads, then asks Jev four questions, batched into one request when the backend supports it (was the bot addressed, does it follow, should it reply, how urgent). For messages without a direct mention, quote-reply or poke, it uses a high and a low reply threshold alongside other checks, and scores in between can be reassessed later; those direct interactions can skip the gate. The README suggests a 3 to 7 day dry run first.
A Claude Code plugin that asks Jev one choice question per message, low, medium or high effort, and applies the answer when Jev is at least 60% sure. It changes effort, never the model, so the prompt cache survives. Subagents are never routed.
An agent runtime where an LLM reads the task, one Jev call answers four typed questions (next action, risk, whether to confirm, whether to reason further) and a local policy engine decides what may run. On 53 versioned test cases it hit the expected action 92.5% of the time against 96.2% for an LLM baseline, at about 1.0 s against 4.0 s, and asked for confirmation on 16 of the 17 cases that needed it.
An eval harness for agent skills: headless agent runs, code checks, Jev judge packs and blind human review; the generic pack asks one yes/no question per skill rule. Graded with the writing pack, the reference skill's v2 run matched its original benchmark's report within 2 points on every layer and in total.
Uses Liquid's open d1-2B as a local System 1 for DeepSeek Harness Browser Use and macOS Computer Use, with approvals and routing. Downscaling wide screenshots before the model reads them cut one decision from 879 ms to 310 ms on a 2508-pixel window, and six consecutive trials all chose a click action. d1-2B itself is under the LFM Open License, not MIT.
Retrieval that walks a document's table of contents, asking Jev one yes/no question per section, then ranks the sections it kept with EmbeddingGemma. On all 6,005 test queries of the public SearchTome release it returned 89.1% of gold sections at 3.6 sections per query, against 84.5% at 3 for EmbeddingGemma alone; its top-1 and top-3 recall equal the embedding model's. Jev alone, ranking flat, scored 54.2 top-1. It needs no training and runs on one 8GB consumer GPU, and failed upgrades are published too.
A course project where Jev chooses the transfer strategy for a CRANE+ arm in a Gazebo simulation, MoveIt 2 plans the path and ROS 2 controllers run it. In three episodes on authenticated Jev decisions with no fallback, Jev picked the home transfer at 0.94 to 0.96 confidence and all three placed the cube, 2.70 to 7.48 mm off target. These are scripted-teacher episodes; the README's separate learned-policy (ACT) tests are not reported as grasp success.
Drives an Android emulator from plain language: DeepSeek V4.1 Flash decides the next step, and Jev triages each screen (loading, error, permission prompt) and guards actions before they run. The three-step demo in the README, Settings to Network to Wi-Fi, cost $0.00019 and took 21 seconds; every step saves its screen, decision, tokens and cost.
Asks Jev one question per message, 300 to 600 input tokens, to tell whether the conversation is on the same topic, a subtopic or a new one, and recommends a session reset only when a new topic wins clearly (p of at least 0.65 and 0.25 ahead), so you stop paying to resend a dead transcript. Checked against live Jev on related, unrelated and short messages.
A desktop notes app for Mac, Windows and Linux that keeps plain Markdown files and runs the open Laya model on your own computer. Each tag is a yes/no question, like "Is this note about a trip?"; Laya answers all of them for a note in one pass and keeps the tags it is confident about. Notes stay on the computer; the only network request is a one-time download of about 1.6 GB of model files.
Lets the Pi coding agent drive real Linux desktop apps on Omarchy (Hyprland Wayland) by reading the accessibility tree and asking Jev for the target element and the operation. It offers 11 Wayland-safe element operations. Jev-driven actions abstain below 0.55 confidence and need confirmation below 0.70, or 0.90 for destructive ones; direct operations on a known element run without Jev.
Asks Jev 200 yes/no questions about one text in a single call and shows the answers as a heatmap. The keyless hosted demo replays recorded answers. VENDOR-REPORTED: about 185 ms for one question, 200 ms for 50, and 150 to 300 ms for 200; seven parallel calls returned 1,400 answers in 282 ms, against 1,092 ms back to back. The author measured Jev 1.14 from a US home connection and has not measured calibration.
An OpenAI-compatible router that filters models by hard constraints before Jev judges which one fits. VENDOR-REPORTED, on its own 30 prompts: tier accuracy was 100% against 53.3% for a heuristic, task-type accuracy 83.3% against 46.7%, and hard prompts sent to a weak model 0 against 14. Estimated savings against always using the strongest model, from token counts and configured prices rather than running the chosen models, were 35.6% against 68.7%; the heuristic's estimate was higher because it under-routed. Decision latency was 295 ms at p50 and 409 ms at p95.
Runs multilingual Laya through a local Go/ONNX service, with a ZCode plugin for risk checks, context selection and 13 MCP tools. VENDOR-REPORTED on an RTX 3060, three runs per variant: total input fell from 359,085 to 239,544 tokens (33.3%) and requests from 16 to 9, but non-cached input rose 21.8%. Laya ranking alone cut total input 9.3% and non-cached input about 2.8%, within the observed range; the author has not established a fresh-token or cost saving. The /decide endpoint took about 26 ms at p50.
Puts open pull requests on a wall and asks Jev eight typed questions about each diff and its tests, using quarkusio/quarkus by default. It keeps the answers separate and never combines them into a score. Right/Wrong feedback records corrections and groups accuracy by confidence as you label answers; no accuracy figures are published yet. Its questions and Quarkus context are adapted from brunobat/quarkus-pr-wall.
Supplies OpenClaw with local typed decisions from LiquidAI/d1-Omni-2B-MLX-8bit on Apple Silicon. It has a native decision provider and a bridge for OpenClaw's TypeSafe integration. The repository name contains Kev, but the runner loads d1-Omni. VENDOR-REPORTED on an M4 Mac mini: about 200 ms per warm batch and about 3 GB of resident RAM.
A scorer for the UK AI Security Institute's Inspect framework that asks Drex whether an output meets your criteria and keeps the probabilities, so you can change pass thresholds after the run. It defaults to drex-max, whose target changes with flagship releases; pin a model for repeatable runs. Drex Max accepts images, while drex-v2.0 accepts text and JSON.
Filters the For you and Following feeds in Chrome or Edge using a plain-language instruction and Jev. It sends one bulk TypeSafe request per timeline response, splitting to stay within input budgets, and removes posts scoring at least 0.90 before X renders them. Errors and timeouts leave posts visible; it checks at most 80 posts per response. The built extension has Chromium tests with X and TypeSafe responses stubbed. No filtering-accuracy figures are published.
Jev sizes a Copilot message as tiny, everyday, large or hardest and sends it to a helper agent pinned to a model. Defaults are Haiku 5.5, Sonnet 5.5 and Opus 5.5, with the hardest helper configured to request high effort. Recorded helper runs used medium effort. The author records live runs of the hook in Copilot CLI 1.0.105 and VS Code 1.144, plus the CLI wrapper. The MCP fallback is tested offline only. Its 54 offline tests passed when checked on 03 Nov 2026; it publishes no savings benchmark.
Evidence
Papers, leaderboards, head-to-head tests, a security test and a hands-on review, then a small test we ran ourselves.
Parallel's PWS-Decisions averages three runs of 300 questions on 29 Oct 2026 (Pacific). Its API with live web search scored 76.22%; Perplexity Decider v1.2 scored 50.89%, OpenAI's Decisions API 41.67% and Jev 1.14 33.44% without web access or supplied external context. Parallel puts uniform random guessing at 42.42%. GPT-6 Luna and Claude Haiku 5.5 with native search scored 97.33% and 96.56%, at $16.52 and $27.43 to $28.79 per 1,000 questions, against Parallel's $1.00; median latency was 7.42 and 6.38 seconds against 0.56. Parallel generated synthetic questions from its own customer topics and search traffic, with separately researched answers and exact-match scoring; refusals count as wrong. It calls this a development challenge set: 100 questions are gated, 200 private, and questions, answers and per-question predictions cannot be redistributed. The runner is MIT. The blog chart labels Baseten Elephant at 32.4% overall and 32.3% public; the repo instead records Mammoth at 45.0% and 37.0%.
Parallel · PWS-Decisions v0.1.0-rc1 · blog, repository results and dataset terms · 01 Nov 2026Source ↗Adding Clef-Flash 9B as a domain router reduced pooled nDCG@10 from 0.4503 to 0.4084 in nicklisac's retrieval experiment. Per-domain LoRAs with the router reached 0.4923; a joint LoRA without it reached 0.4947. Perfect domain assignment to specialists gave an oracle ceiling of 0.4991. The detailed report and logs use 2,161 held-out queries over FiQA, ArguAna, SciFact and CQADupstack-gaming, with EmbeddingGemma 2 on one RTX 3090; the README says 1,081. Post-retrieval reranking and pruning with Clef-Flash also reduced the corresponding embedding-only scores.
nicklisac · lora-router-rag · REPORT.md and results/run_log.jsonl · checked 03 Nov 2026Source ↗Using Tev1's own decision protocol and v1 evaluation data through local Ollama, jev-rsi's author measured untuned Qwen3.5-4B at 673/1,000 (67.3%) on test and 130/300 (43.3%) on policy_transfer. Together reports 880/1,000 (88.0%) and 300/300 (100%) on evaluation sets used during development (VENDOR-REPORTED). In a separate frozen-backbone experiment with small heads and a fixed self-improvement loop, test scores ranged from 6.7% to 31.1%, against a 29.8% random baseline, on a small held-out set. The author concludes that heads alone did not learn the decision task in this setup.
Joshhuang123 · jev-rsi · baseline and frozen-head experiments · 02 Nov 2026Source ↗EO-Jev adapts d3-flash (8.4B, Qwen3.5-9B backbone), from the Decision 1.0 makers, with LoRA for Earth observation. Across 30 settings from 23 public benchmarks, its authors report significant improvements in 26 settings, averaging +23.8 accuracy points in-domain and +8.0 on 12 held-out settings. It matched or exceeded the best open EO specialist in 20 of 28 settings and had lower calibration error in 21 of 28. Median throughput was 6.4x the specialist's with each at its best batch size, or 3.2x with one question per call. General decision accuracy fell 1.8 points. Counting, change quantification and calibration far from the training data remain weak spots. The repository has code and reports; it excludes weights and run logs, and lists the paper as coming soon.
Abouelhadid, Abuhani, Munir, Khan, Khan · MBZUAI and collaborators · EO-Jev · 02 Nov 2026Source ↗With two reference images per class across five manufacturing case studies, Jev scored 0.75 to 1.00 test accuracy and Clef-Flash 0.71 to 0.99, against CLIP's 0.67 to 0.84 using the same references. The authors' agentic description refinement improved accuracy in six of eight model/application combinations, and their calibration reduced Brier score in 13 of 15 configurations.
Megahed, Chen · Beyond CLIP · arXiv 2610.41199 · 30 Oct 2026Source ↗VerdictAD uses Clef-Flash 9B to refine existing anomaly detectors with decisions about whole images and local regions. In the authors' four-shot tests, averaged over three reference draws, PatchCore's image AUROC rose from 93.0 to 97.5 on MVTec AD, 89.0 to 93.9 on VisA and 67.4 to 78.0 on MVTec LOCO AD. DINOv2-L rose from 68.7 to 77.5 on LOCO. These are image-level AUROC figures, not accuracy or pixel-level scores; the repository includes the paper and experiment logs.
Sun et al. · VerdictAD · code, paper and logs · 02 Nov 2026Source ↗One developer trained a decision head and LoRA on Qwen3.5-0.8B with Unsloth to screen job postings. On 63 receipt-labelled test examples, accuracy was 0.762 and macro-F1 0.683, against 0.714 and 0.278 for always rejecting. On a separate 460-posting check against an agent's historical verdicts, accuracy was 0.726, below the 0.835 always-reject baseline. That external check uses a different population and reference. The author's data and trained weights stay private, so a public clone cannot reproduce the scores.
jayvir-git · training-own-decisions-model · 02 Nov 2026Source ↗A medical LoRA on Qwen3.5-4B scored 67.90% on 1,514 external questions, against 59.91% for the base with the same scoring method and 70.87% for archived Jev 1.13. The full-set score counts 100 questions with 148 options as wrong because the released interface supports at most 52. On 100 triage questions it answered 40 correctly, against Jev's 66. The maker has not scored 1,620 sealed questions; training labels lack individual clinical-expert review and the probabilities are uncalibrated.
JuneYao · medical-jev-4b · model card · 02 Nov 2026Source ↗PathoJev encodes a pathology slide once and answers several questions over that shared visual context. Across three whole-slide benchmarks, the authors report mean accuracy of 77.2% for 9B and 78.1% for 27B, exceeding SlideChat by 5.4 and 6.3 percentage points. The 9B model ran 9.1 times faster than SlideChat on WSI-Bench. Against encoding the prefix separately, shared context was 6.9 times faster for eight questions and 11.2 times for 64 at 9B; the 27B figures were 5.9 and 8.9 times.
Zhang et al. · PathoJev · arXiv 2610.40752 · 29 Oct 2026Source ↗DecisionRank trains a decision reranker by having a generative teacher judge ranked lists built from the student's scores. The authors report that a 1.7B student beat its 27B generative teacher on TREC DL19 to DL23. At 8B, it beat its generative listwise counterpart while running 24.5 times faster on TREC DL and 16.7 times faster on BRIGHT.
Wu et al. · DecisionRank · arXiv 2610.42128 · 30 Oct 2026Source ↗Operon-S1 turns recurring judgments in a biomedical agent into closed questions at 13 decision points. A local decision model answered in-loop requests at a median 0.6 seconds on CPU, with no measurable agent-score change on five of six benchmarks. Asking the driving LLM those same questions instead accounted for 8.0% to 35.2% of task cost. Genoria AI's study spans seven life-science benchmarks overall; the unchanged-score comparison covers six, and task-level results use the public checkpoint.
Hou et al., Genoria AI · Operon-S1 · arXiv 2610.41350 · 30 Oct 2026Source ↗Liquid's open 2B decision models against two 2B chat models (Qwen3.5-2B, InternVL3.5-2B) on 248 real pictures, all on one RTX 5060 Ti. d1-2B routed scanned documents to one of 16 types at 88.8% against 55% and 56%; d1-Omni-2B classified 71 of 73 clear frames correctly as violent or not (97.3%); on a 50/50 defective-part set the d1 models scored 70.8%. d1 was 1.6 to 23 times faster per question. The author notes it is not quite like for like: the chat models reply in text. On 500 text items d1-2B agreed with Jev's answers 68% of the time.
Aditya (@0xironyAditya) · d1-2B vs three 2B models · 01 Nov 2026Source ↗Jev as a search reranker against Cohere, Voyage and Qwen rerankers on multilingual MIRACL and Japanese JQaRA. Scoring each passage with its own yes/no question, batched per query, Jev 1.14 scored nDCG@10 0.7496 on MIRACL and 0.8849 on Japanese JQaRA, the best mean of the final seven (0.8173, against 0.8061 for Cohere rerank-4-pro), at $0.89 to $1.14 per 1,000 queries against Cohere's $2.50. In a separate 252-query MIRACL run most dedicated rerankers beat Jev 1.13 by a significant margin; results differ by dataset, model version and scoring method.
Jeffrey (Dongkyu) Kim · jev-reranker · 01 Nov 2026Source ↗Decision models playing Super Mario Bros. world 1-1, one choice per step. From the game state, Perplexity's Decider v1.2 cleared the level in 20.13 game seconds for $0.0078, and Liquid's d1-ultra and Inception's Mercury Decide also cleared it. From pixels alone, Decider v1.2 and Baseten's Mammoth 1 cleared it; Jev 1.14's best pixel run reached 51.94% of the level. These are 46 development runs with different settings, which the author says are not a ranking.
Nicholas Greenwood · mario-bench · 01 Nov 2026Source ↗On the 127 test documents of the Text Anonymization Benchmark (European Court of Human Rights cases), Cloudflare's extraction model Clairic flagged more identities than OpenAI's Privacy Filter but was less precise: 77.7% of its spans were ones annotators marked to hide, against 93.4%. Passing each Clairic span to Jev to decide hide or keep raised precision to 91.1% while keeping Clairic's recall (96% on applicant names and case IDs, where Privacy Filter found 77% and 2.2%). It cost the most: $0.86 per 1,000 judgments against $0.26 and $0.17. Legal documents only.
Arjun Kumar, Clavia · Detecting private data using extraction + decision models · 31 Oct 2026Source ↗13 decision models against 15 generative LLM setups on 1,273 four-option MedQA questions. Decision models scored 67.71% to 89.08%, the generative setups 78.32% to 96.86%. Mercury Decide was cheapest at $0.0069 per 1,000 questions and Clef Omni fastest at a median 0.291 seconds. Answers given a probability of 0.90 or more were still wrong 1.71% to 12.77% of the time, depending on the model.
Freyer et al. · Decision models vs generative LLMs on medical questions · arXiv 2610.38448 · 28 Oct 2026Source ↗Jev and five open decision models on gender-swapped yes/no decisions about people, BBQ and StereoSet. Five of the six gave women significantly more favourable answers than men (+0.05 to +0.10 in log-odds), and swapping gender changed 1 to 3.5% of top answers. The makers' recommended confidence gate removed many biased answers but raised the stereotyped share of those it kept on ambiguous BBQ gender questions.
Havinga · When the Top Answer Decides · arXiv 2610.39747 · 29 Oct 2026Source ↗A team put Jev into a multi-agent build pipeline for Claude Code for 16 days, about 5,300 calls, as advisory checks, an on-demand tool and binding gates. It was accurate where the majority answer was enough and mixed elsewhere; 172 of 240 scope judgments came back uncertain or with insufficient evidence. On the claim-support gate, a strong model solved the task from the same excerpt, so the gap was Jev's. They removed it and never measured the token savings they were after. The anonymised data is published, with a script that rechecks 103 of the reported figures.
sash-ua · Accurate and Useless · 30 Oct 2026Source ↗Squigdle, a daily drawing game, sends every doodle to Jev as a 512×512 image plus its title and acts on the probabilities with its own thresholds. After about 1,000 doodles the misses are the occasional false alarm, and moderating all of them cost 6 cents in total, about $0.00006 a doodle. These are one developer's figures from a live game.
James O'Halloran · Moderating 500 daily anonymous doodles with Jev · 30 Oct 2026Source ↗Across 20 jobs drawn from TypeSafe's 21 official cookbooks, plus image tests, Jev got 155 of 163 core examples right and 132 of 144 edge cases built to push its known weaknesses. Its main failure: when the right answer isn't an option, it picks the nearest one, often confidently. A replay after the locked tests, adding only a "none of these" exit to the menus, raised correct handling from 11 of 14 requests to 13. 1,463 requests cost $0.0331. Small hand-written samples, not a benchmark.
WeBuildDatabases · Jev, job by job · 29 Oct 2026Source ↗In a simulated moderation queue, a wave of privacy-invading posts arrived that the existing router did not prioritise. Claude drafted one yes/no question in 12 seconds from 20 of those posts and 20 ordinary ones, and a fine-tuned version of the open decision model Laya asked it of every new post. At 100 extra posts an hour, the queue needed one extra moderator instead of three, the same staffing as an oracle rule; the routing threshold was set with labelled calibration data. In a separate test, base Laya answering a fixed money-back question scored AUC 0.68 on consumer complaints, against 0.89 for the complaint's product field. Three seeds; staffing counted in whole moderators.
Enayat Hassani · Exposure-aware routing · 29 Oct 2026Source ↗Put in front of a small LLM (Qwen2.5-1.5B) as an input gate, Jev cut how many of 720 harmful-labelled prompts got an answer from 527 to 17, but refused 27 of 250 safe prompts, against 3 for the LLM alone. A tiered gate that also reviewed borderline answers let 40 through and refused 10 safe ones. Answered counts are a proxy for harm, not checked completions.
Cognetti, Krishnamachari (USC) · Jev as an LLM Safety Guardrail · 2026, code and tablesSource ↗Asked about video, Jev 1.14.0 answered 463 of 658 native video questions correctly (70.4%), but pinning down when an event happens was weak: 10.0% mean average precision with three ordered frames, against 0.3% for a basic CLIP baseline, at temporal IoU ≥ 0.50 on the study's mapped action subset.
Eventum · Jev Video Lab, first study · 28 Oct 2026Source ↗One identity word moves Jev far more than a name does. On jev-1.13.0, "You are a Republican. Do you want to sign a carbon-tax petition?" got P(yes) 0.15; the Democrat version 0.73. Explicit labels shifted decisions by 3 to 15 percentage points, while Black- vs White-associated names moved them 0.2 (95% CI -0.6 to +1.0). Offering an "equal" option shrank most race, gender, religion and orientation gaps, but large gaps remained for age, undocumented status, class and atheism. 134,772 answers across jev-1.13.0 and 1.14.0, for USD 0.32.
Isaac Bitran (Brown University) · Jev bias audit · Oct 2026Source ↗On facts about other companies, Parallel's web-searching Decisions API beat Jev answering from memory: 83 to 98% vs 43 to 70% balanced accuracy on four questions about 50 YC companies. Across that test and a 30-topic publishing test, Jev put 116 of 230 answers between 0.4 and 0.6. On "have we already published this?", handing Jev the 10 closest sitemap URLs took it from 67% to 97%, at about 3% of Parallel's price. One run, one day, small sample.
Vedant Bhayani · Jev vs Parallel on two of Eric Siu's Jev gates · 28 Oct 2026Source ↗Five decision models judged 9,852 items for five kinds of hallucination against gpt-6-sol. The best matched it on context inconsistency and came within 3 points on factual errors, but trailed by 4 to 8 on logic and instruction-following. A confidence cascade of decision models matched or beat gpt-6-sol on three types at 1 to 3% of its cost.
Liu, Xue, Wang, Mu, Ma, Feng, Li · Decision Models as Judges for Hallucination Detection · arXiv 2610.37229 · 27 Oct 2026Source ↗Asked why retrieved sources disagree, 10 of 11 decision models from 7 providers matched or beat Gemini 2.5 Flash (61.3%), at 4 to 39x lower billed cost (two were free) and 2.6 to 6x lower latency. Gemini 3.8 Flash (70.0%) was more accurate than 8 of them, but sending each model's least confident quarter to it was statistically indistinguishable from Gemini 3.8 Flash for 8 of 11, at 25 to 37% of its cost.
Khatri, Jain, Kapoor, Sah · Do We Need an LLM to Know Why Sources Conflict? · arXiv 2610.36915 · 27 Oct 2026Source ↗When a collection already has labelled examples, learning from them beat Jev, which labels each document cold. Across 29 classification tasks, Voyage's experimental voyage-json embeddings plus the raw fields, fed to a ridge regression trained on the labelled documents, averaged 88.3 AUROC against Jev's 77.6; Voyage counts that as 48% fewer errors (error = 100 minus the score). Voyage's own test of its own models.
Voyage AI blog · How to create your own Jev using Voyage models · 27 Oct 2026Source ↗Shuffling the order of the answer options across 1,700 questions barely moved Jev's accuracy (under two points), but the set of questions it ever got wrong grew by 27 to 29%, more than plain repetition caused. Across two Jev versions, twelve Jev reproductions and six generative LLMs, Jev showed no general advantage in keeping correct answers.
Song, Zheng, Ma, Zhou · Understanding Jev-Style System One Models: Does Structure Reduce Selection Bias? · arXiv 2610.35826 · 26 Oct 2026Source ↗Screening 2,048 patient-condition pairs for clinical trials, with questions batched per patient, Jev matched Qwen Flash (thinking off) on two datasets (macro-F1 0.652 vs 0.660, 0.807 vs 0.814) and beat it on MIMIC-III (0.768 vs 0.534), mostly by flagging when a record lacked the information (56.3% recall vs 9.9%). In batch mode it was faster and an estimated 53 to 74% cheaper per condition; asked one condition at a time, the cost edge did not always hold.
He, Gao, Li, Zhang, Wei, Zhang, Yuan · Can JEV Support Clinical Trial Criteria Assessment? · arXiv 2610.35721 · 26 Oct 2026Source ↗DecisionRAG uses a decision model to browse a corpus from broad topics down to passages, with no first-stage retriever and no task-specific training. It beat dense retrieval with an 8B reranker by 13.8 R@1 points on MultiDoc2Dial and 17.4 Hit@1 points on Natural Questions, with median query latency of 0.68 to 1.76 seconds across four benchmarks.
Wan, Jünger, Du, Liu, Krishnan, Weinberger · Retriever-Free Retrieval: Navigating Documents with Decision Models · arXiv 2610.35868 · 26 Oct 2026Source ↗Across six Qwen backbones from 0.6B to 27B and ten TREC DL and BEIR collections, decision fine-tuning never made the better reranker. Up to 9B, plain reranker fine-tuning won by 5 to 29 points nDCG@10 and decision fine-tuning added nothing over asking the untuned model; at 27B the two tied. The authors' advice: if you need a reranker, fine-tune a reranker.
Dinzinger, Ghosh Dastidar, Granitzer · System One models Are Not Better Rerankers · arXiv 2610.32318 · 23 Oct 2026Source ↗On an owner-labelled Italian and English inbox (231 emails in the main comparison), logistic regression trained on about 200 labels had the highest accuracy on every field, though statistically tied with Gemma 4 on message type and with hosted Nimble on urgency, ahead of local decision models (Tev1, Nimble). When used to choose actions, it ignored emails that needed action more often than hosted Nimble or Gemma 4 (18% against 4% for the best LLM), until a cost-chosen threshold halved that.
Federico Lucca · Measure Before You Train · arXiv 2610.32955 · 23 Oct 2026Source ↗PathJev-P, a decision head on MedGemma-1.5-4B, beat a generative model trained on the same backbone and data, the pathology model CONCH and a general medical decision model on balanced accuracy (0.541 against 0.492, 0.345 and 0.213). Its calibration edge depended on how the generative model's probabilities were read, and a simple linear probe on a pathology foundation model was better calibrated in every setting.
Tommasino, Rinaldi · PathJev · arXiv 2610.32478 · 23 Oct 2026Source ↗Grading 200 held-out Amazon shopping-search results, Jev 1.14 agreed exactly with the human graders 55% of the time, against 43.5% for the best LLM tested (Gemma 3 27B), at a median 118 ms against the LLMs' 435 to 656 ms. Asked to rank eight search setups, Jev ordered 26 of the 28 pairs the way the human labels did.
rickcrawford · jev-relevancy, on Amazon's ESCI shopping queries · Oct 2026Source ↗Logistic regression with temperature scaling, rebuilt behind Jev's interface, matched Jev's accuracy on a 7-intent banking slice (0.975 each; Jev's calibration error was lower, 0.014 against 0.022, but the gap was within noise) and beat it on all 77 intents (0.888 against 0.859 at best). Jev's choice answers were well calibrated; its yes/no answers were underconfident on true cases.
ankur287 · jev-open-calibration, code and data for a Towards Data Science article · Oct 2026Source ↗Four decision models on 13 Vietnamese classification settings. With English label descriptions, Jev ranked first by macro-F1 in 11 of them. The weak spot was diacritics: strip the tone and vowel marks and each model's mean macro-F1 fell 9.5 points (Jev, the least) to 15.5 (Quyet), against 0.5 to 1.1 for slang and 2.9 to 4.5 for typos.
Ha, Phuc, Quy, Tran · How Well Do Decision Models Transfer to Vietnamese? · arXiv 2610.31422 · 22 Oct 2026Source ↗Swap the correct case holding out of the options and see who admits none fit. Six generative LLMs still picked one in 38-86% of those items; the most cautious of four decision models did so in 30%. On the standard test, a decision model matched the best LLM (88.1% vs 88.2%) at 1/55 of the cost, and accepting the LLM's answer only when a decision model agreed halved its wrong citations.
Demir · Commit or Abstain? · arXiv 2610.29961 · 21 Oct 2026Source ↗As a judge of 118 paediatric clinical AI answers, Jev's agreement with physicians matched or beat seven of 12 LLM judges, including Claude Opus 5.5 and GPT-6. Frontier judges cost 73 to 294 times more and took 7 to 57 times longer.
Gershon, Cohen, Ariel, Bilavsky-Yarden · Decision language models as alternatives to LLM judges · arXiv 2610.28826 · 20 Oct 2026Source ↗Artificial Analysis launched a Decision Index covering knowledge, reasoning, tool calling and safety classification. At launch Jev 1.13 led the decision models with 58, ahead of GLiDE and Microsoft-Decision-1 on 54. Its sharpest finding: decision models are overconfident.
Artificial Analysis Decision Index v1.0 · 13 Oct 2026Source ↗On claim verification Jev ties GPT-6-class models at 0.975, for a fraction of the cost. On Vals AI's LegalBench sample Mercury Decide beats it, 0.869 to 0.740. Given a 1% error budget on claims, Mercury automated 94% at 0.8% error; Jev automated 95% at 1.6%, over budget.
Vals AI · Jev evaluation 6 Oct 2026, Mercury Decide evaluation 8 Oct 2026Source ↗Cut an LLM's reasoning short, then let Jev pick the answer from the half-finished trace. Across 35 reasoner-benchmark pairs, Jev's mean accuracy beat forcing the LLM to answer at every budget tested, from 500 to 16,000 tokens; at 1,500 tokens it added 4.1 points. In offline replay on held-out questions, a tuned stopping policy was estimated to save 71.3% of the full run's cost for a 0.58-point accuracy loss.
Wang et al. · Truncated Reasoning Under a Budget · arXiv 2610.25768 · 19 Oct 2026Source ↗The study compared Jev, Tev1-4B and Nimble-9B with two trained classifiers, with class frequencies shifted in simulation; the two open models were also run against their own Qwen backbones. Decision training was not uniformly better: Nimble was better calibrated than its base on intent tasks but scored 12.0 Macro-F1 points lower on financial sentiment. The decision models were not uniquely fragile under shift, though single shifts could still push threshold risk up sharply; their main weakness was miscalibration they already had.
Iqbal, Nasim, Saleem · Typed Decision Models under Prevalence Shift · arXiv 2610.26325 · 19 Oct 2026Source ↗“In this early literature, the typed readout itself has not shown an independent accuracy advantage over comparable label-probability readouts.”
A review of the 28 papers posted in the nine days after Jev launched on 15 Sep 2026. Jev's clearest gains were latency and cost, with accuracy gaps on harder tasks. The authors call it an early evidence map, not a verdict.
Typed Decision Models: An Early Evidence Audit · arXiv 2609.32160 · 26 Sep 2026Source ↗In SILT's preregistered test of 799 unpublished yes/no questions about coding-agent work and destructive shell commands, Perplexity's open Decider V1.1-27B was the most accurate (94.7%) and the only one of seven models to pass SILT's chosen calibration rule. Jev's result was inconclusive. So were the results for OpenAI's Decisions API and Laya.
SILT · Do Decision Models Know When They're Wrong? · 11 Oct 2026Source ↗Used as a prompt-injection detector, Jev caught 82.90% of labelled attacks. Prepend a fake policy inside the untrusted text and it caught 3.88%. The authors separate this classifier bypass from a full agent compromise.
Datadog Security Labs · 15 Oct 2026Source ↗Ask "is it X?" and "is it not X?" and the two probabilities should add to 1. Jev misses by 0.064 on average. Reading the first token of an open 27B model misses by 0.293. Neither is fully coherent.
Li, He, Li · Beyond Calibration · arXiv 2609.33209 · 27 Sep 2026Source ↗JevBench ranks Jev-class systems on intelligence, calibration, speed and cost. By 21 Oct 2026 it had measured 252 of them and ranked 137 open-weight systems. Chon-1.0-12B tops its capability table at 85.3, though whether its weights are open is listed as unknown; the best confirmed open-weight system, Quyet-1.0-Large, scores 81.7. Jev 1.13, kept as an unranked reference, scores 77.1.
JevBench v1.6.37 · Benchmark Heaven · 21 Oct 2026 · not affiliated with TypeSafeSource ↗Useful for ranking and classification, but it can carry bias you can't see: asked whether Bay Area cities were good, Jev put Cupertino top and East Palo Alto bottom. With only a number coming back, that's harder to spot.
Simon Willison · 21 Sep 2026Source ↗Without any decision fine-tuning, the instruction-tuned Gemma 4 31B already scores 79.6 on the authors' new OpenDecisions set, against Jev's 80.1. The authors' published recipe makes an open Jev possible: after following it, the model scores 84.4 (the authors' own numbers).
Geuter, Cheng, Alvarez-Melis · Releasing the Inner Jev · arXiv 2610.17205 · 12 Oct 2026Source ↗Political-science coding tasks from published studies, re-run on Jev, GPT-6 Luna and an open Qwen3.8-27B. Jev matched or came close to both, but had no cost edge over OpenAI's batch prices, and its probabilities were better calibrated than Luna's but not consistently better than Qwen's. The authors' verdict: unless you need speed, the main advantage is ease of parsing.
Denney, DiGiuseppe · JEV versus LLMs · arXiv 2610.06625 · 6 Oct 2026Source ↗Our benchmark · v0.1
Generated JSON
97.7ms
11 output tokens
Schema-constrained
74.4ms
7 output tokens
Direct readout
24.6ms
1 output token
The fastest path was also the least varied. On this 0.6B model the direct readout answered technical in 69 of 75 calls and never once said billing, on a set that includes "my card was charged twice". Without labels we can't score accuracy, but an answer that barely changes is a warning sign. Whether a purpose-trained decision model avoids this is the open question.
Medians over 75 timed calls per path. This measures mechanics, not accuracy: there are no ground-truth labels, so "disagree" means the paths differ, not that one is right. Local latency says nothing about any hosted API. Raw results · Summary
Lineage · 2017 to 2026
When Jev launched on 15 September 2026, a GLiNER2 co-author answered by pointing readers to weights already on Hugging Face. The papers trace which parts of the idea predate Jev and which are new. Every entry is checked against arXiv or Hugging Face.
Resources
TypeSafe AI's launch post, 15 Sep 2026, gave the category its name.
Simon Willison's hands-on write-up, 21 Sep 2026. The clearest independent explainer.
Joe Barrow, an NLP researcher at Adobe Research, explains how decision models work through an open one, Perplexity's Decider v1.2: the architecture, how it is served, what its design says about training, and when to use one. An X article, 30 Oct 2026.
Interview with Diogo Almeida, TypeSafe CEO, 21 Sep 2026. The fullest statement of their thesis, including RLCD.
Nearly 2,000 points and over 500 comments. The sharpest pushback is in here.
Reviews the first 28 papers and proposes a 14-item evaluation checklist. 26 Sep 2026.
A label-free coherence test, run on Jev and an open 27B model. 27 Sep 2026.
An exploratory case study of Jev inside an LLM pentest agent, plus a discussion of Laya's published specs. 24 Sep 2026.
Native /v1/systemone support on your own machine, from 28 Sep 2026.
Typed option probabilities straight from an open model, in your browser. No waitlist.
Jev-style parallel constrained decisions for any MLX model on Apple Silicon; typed schema-valid JSON in one forward pass.
Browser decision playground and reproducible experiments on memory, uncertainty and control.
Community-maintained list of TypeSafe, System One and Jev resources. Scoped to that ecosystem, not a general decision-model index.
Another independent directory of decision models, with recipes and guides.
Where this site posts new releases first.
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