Article · Benchmarks & research
Auto-optimizing Jev: Half the Errors, 1/7 the Cost
Kiln points its autoresearch loop at Jev for Mandarin pinyin disambiguation.
Open kiln.tech ↗How builders describe it
Kiln points its autoresearch loop at Jev for Mandarin pinyin disambiguation. The optimized harness, built from jieba segmentation and CC-CEDICT word evidence with some chained Jev calls, cuts mistakes from 316 to 147 on 61,750 decision positions, ahead of a DeepSeek reasoning baseline at $1.60…
The decision Jev makes
Benchmark questions with known answers, to check accuracy and confidence.
Where it fits
Head-to-head tests, calibration studies and independent research into how well Jev decides, how fast, and at what cost. All 682 benchmarks & research projects →
Related projects
Benchmarks & researchMicaA 4B decision model: calibrated probabilities for yes/no, choice and score questions over long inputs.GitHub · ★ 37 · akivet
Benchmarks & researchawesome-jev-papers📄 Research papers on Jev, TypeSafe's System One model, and the open models built in its shape.GitHub · ★ 5 · OmniJev
Benchmarks & researchjev-typesafe-real-financial-use-casesFifty real-world financial use cases for TypeSafe's Jev model: typed, structured LLM answers over ledgers, fraud, portfolios, trades and filings,…GitHub · ★ 3 · IslamBaraka90
Benchmarks & researchjev-healthcare-labOpen experiment comparing TypeSafe Jev vs DeepSeek on 96 healthcare tasks across 12 scenarios (quality, latency, cost) with per-task data and…GitHub · JuneYaooo
Benchmarks & researchtypesafe-jev-ecosystemExplore the TypeSafe Jev ecosystem: open-source integrations, compatible decision models, SDKs, agent tools, and research.GitHub · ★ 4 · VoltAgent
Benchmarks & researchBeelineBenchHow well do decision models plan across a multi-step problem?GitHub · ★ 3 · kyle-pena-nlp