Calibration & Research

Character-level language model built from Jev Choice over 28 options, where each option is the full hypothesis text.

Research replicating Jev-style typed decisions with open LLMs via logprobs; Qwen3.5-9B reached 94% with good calibration in ~1 s.

Runnable yardstick that tests Jev's three headline claims on a small local model: typed output, no hallucination, calibration.

JevNext: decodes numbers from Jev by walking a multiway decision tree of interval or digit Choice calls, without logits or training.

Open-weights Jev-style decision engine on Qwen2.5-1.5B with a decision head and QLoRA, answering Choice, Noul and Score in one pass.

Independent open model on Molmo2-ER that answers spatial questions by choosing from fixed options with one shared classifier head.

Local reproduction of Jev-style inference that reads option-token logits from a small LLM in one pass, with an honest failure log.

Tool that recomputes Jev's confidence identities on outputs published in public repositories to spot answers Jev never actually produced.

Korean study notes and TypeScript mock-ups of Jev concepts, with a local Laya versus GPT-5 comparison and a calibration toolkit.

Experimental engine that makes an existing LLM do Jev-like classification and scoring by reusing context and building JSON.

From-scratch research reconstruction of a small System-One decision model, used to route and gate commands in a Windows palette.

Offline Python toolkit that measures sensitivity, Fisher information and information loss in the probabilities of Jev-style agent systems.

Python kernel of Jev Choice/Score/Noul gates, caches and loops used to admit or reject Lean proof candidates in research pipelines.

C/C++ compiler driver where Jev decides whether to inline each discretionary call site, compared against Clang -Oz.

Experiment that uses Jev as a sampler: DeepSeek proposes top-k next tokens and Jev chooses which comes next and when to stop.

Research fork of Laya that reads typed choice and yes/no decisions from an image plus optional text in one forward pass.

Open decision models that derive calibrated confidence from recurrent looped-layer dynamics instead of RL, with early exit and a Jev API.

Experiment that makes Jev produce prose by asking one choice question per word over a routed domain vocabulary.

Research implementation of Jev-style typed decisions over clinical evidence: supported, contradicted or unresolved.

Inference-time loop where a System One judge like Jev scores a model's thought paths and picks which reasoning branch to keep.

Open Jev-class decision models on Qwen3.5 with candidate-logit readout; the 4B model reports 80.1% on JevBench's 231 items.

Reproduction and stress test of a paper asking whether Jev-style probabilities stay calibrated after routing on selected examples.

OEV: 184M-parameter decision engine that scores options in one pass, reporting 0.7705 on typed-decisions and 22 ms per question.

Open System-1 (OpenJev): Apache-2.0 research models giving calibrated per-option probabilities, best is a 4.9B exp9a.

Reads typed decisions from one forward pass of PrismML's 1.75-bit Bonsai 27B under llama.cpp, using the OpenJev/SemIf logit method.

From-first-principles PyTorch reconstruction of a Jev-like architecture with typed noul, choice and score heads; random weights only.

Open JEV: local bilingual (English/Chinese) choice and binary decisions read from a frozen Qwen3-4B's next-token label distribution.

openvons (open-Jev): finite-choice decision layer for text, images and Japanese voice, with a none option and execute/confirm/reject.

Java (llama.cpp via FFM) and Python engines that fill boolean/enum JSON schemas in two forward passes with per-field confidence.

Independent reproduction of Jev-style decision-model post-training on MiniCPM5-2B with LoRA, a sealed holdout and stated limits.