Skip to content

jev-local-research

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

Projectresearch

Overview

Local Jev: typed decisions with open LLMs asks whether Jev can be replicated locally without an API key and how well it works. The answer it gives is yes: Jev's approach can be reproduced with any LLM that exposes log probabilities, by labelling options with letters, asking for only the letter, and reading the probability of each letter at the first position as the decision. This takes one forward pass, gives explicit probabilities for threshold routing, and allows options defined at runtime.

It compares SemIf with other local setups on authored144, a 144-decision English set from the SemIf repo covering evidence interpretation, rule application and candidate selection, using the same prompt for every system. The README reports that SemIf adds nothing over reading logprobs directly, and that the best local result was Qwen3.5-9B at 94 percent accuracy with good calibration and about one second per decision.

What the decision model decides

TypeQuestionAnswers
ChoiceDoes the evidence support, contradict or not settle the claim?support · contradict · not settled
Yes / noDoes the rule apply?—
ChoiceWhich candidate meets the criterion?—

How it works

  1. Label options with letters in a fixed prompt
  2. Run local LLMs and read first-token letter probabilities
  3. Score accuracy and calibration on authored144
  4. Compare SemIf with direct logprob reading

Strengths

  • Shows a simple local replication path
  • Same prompt across systems
  • Accuracy and calibration reported

Caveats

  • Single 144-item test set
  • Latency depends on local hardware

At a glance

Type
Project
Status
research
Language
Python
Runtime
Local LLMs (MLX, Docker Model Runner)
Has code
yes

Use cases and integrations

How curated lists describe it

Frequently asked questions

Can Jev be replicated locally?

The study says its approach can, using logprobs from any open LLM.

Which local model did best?

Qwen3.5-9B at 94 percent with good calibration.

Does SemIf help?

The README reports it adds nothing over reading logprobs directly.

Related projects

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

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

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

    ProjectresearchPython
  • Laptop-scale reimplementation of RL for Calibrated Decisions: parallel constrained decoding trained with GRPO and a Brier reward.

    ProjectresearchPython
  • AnyJev★ 1,255

    Nokia's training-free library that turns open LLMs into Jev-style deciders robust to option order, plus self-distilled Tacit models.

    ModelresearchPython
  • Jev-style replica: frozen Qwen3-1.7B plus a ~0.8M-parameter trained head returns calibrated Choice, Boolean and Score distributions.

    ServiceresearchPython