Calibration & Research

Open clinical text classifiers (0.8B-9B on Qwen3.5) with labels set at inference time, run locally and compared with Jev 1.13.

Pre-registered field test of Jev, GLiNER2.5-Decide, Laya and other decision models against small LLMs on fresh September 2026 data.

11 MB CPU-only model that answers choice, noul and score questions with fitted calibration and no LLM at inference.

Official code for the FastJEV paper: training-free reduction of redundant candidate computation in multimodal JEV-style models.

Tiny pre-registered experiment testing whether a confidence-scoring reward (RLCD-like) yields confidences safe to automate on at 0.9.

Research code for an image-and-text System One model that reads option-letter logits from a multimodal LLM, with training and calibration.

Instruction dataset of 119 rows compiled from TypeSafe's public Jev documentation, split by Choice, Noul and Score.

Local server (xks) implementing Jev's /v1/systemone contract on an open-weight LLM, accelerated with Xeon Phi cards.

A chatbot with no language model: every word is chosen by Jev Choice questions from thousands of options, with lookahead and self-critique.

Repository serving CLM-8B, a contrastive System One model behind a TypeSafe-compatible API, aimed at humanoid robot decisions.

Research MVP pairing an LLM-authored, revisable ontology with Jev classification, where low confidence triggers ontology revisions.

Builds a Jev-style decision interface on Gemma 3 270M and measures what it takes: LoRA plus a pointer head reaches 0.821 AUC.

Research runtime that lets a bounded decision model like Jev operate over large tool, memory and context spaces via a virtual option space.

Black-box reverse-engineering archive studying how the Jev decision model behaves, with evidence matrices and calibration analysis.

Educational C11 library and CLI that turns DiffusionGemma 26B into a CPU-first OpenJev-style decision engine reading candidate logits.

Independent API-only audit of what Jev's probabilities mean: abstain removal, Korean, coherence, option order, interference and caching.

Exact-target audit finding Jev's Choice probabilities are a step function on stated base rates while per-option Noul tracks them closely.

Research code showing confidence cascades let a 0.8B prefill-only decision model approach a 4B model's accuracy at a fraction of the cost.

Research decoder that treats Jev as a language model, assembling replies from repeated choices over phrases, words and letters.

Research framework (DART) testing whether Jev's probabilities are a reliable signal to answer, escalate or abstain.

Experiment training two tiny Jev-style decision models, linear recurrence versus attention, under identical conditions.

Playful experiments using Jev's Choice as a decoder: next character, next BPE token, and grammar-constrained SQL generation.

Position paper arguing per-hop calibration does not compose in decision pipelines and proposing deferred crispification.

Reads Jev-style choice, yes/no and score probabilities from masked diffusion language models with shared Yes/No weights.

Evidence ledger auditing Jev claims, recomputing published calibration and running an 800-item difficulty-gradient study.

Research demo where Jev answers plain questions about text and a small learned head adds new questions from human feedback.

Research code using the Canopy-Jev decision model to route tokens to experts and extra recursion steps under explicit budgets.

Experiment generating chat replies character by character with Jev Choice over 98 next-character continuations.

Text generation with Jev via cascaded Choice questions: word type, WordNet category, embedding cluster, then the word itself.

Word-level language model whose output layer is Jev: next-word choices, Noul-verified drafted chunks and bits-per-token evaluation.