Community project / Evaluations and independent research

TetraJev

MIT-licensed, locally-deployed decision layer for complex bounded decisions: two frozen open-weight readers, four readings (letter + per-candidate yes/no) fused fit-free and routed by agreement into auto-release vs human review; eight decision suites and the RAG reranking pass benchmarked — including DecisionBench's 35 real-world task categories — with coverage–accuracy curves. It does not call the TypeSafe API, and its equal-weight fusion still trails the strongest single reading at some high-precision operating points.

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  Open local Choice, Noul, and Score model with public weights, training code, and a System One-shaped API. Benchmark comparisons are author-reported: its README gives a 91.23% accuracy headline without a matching result artifact in the repo, while its 49-task table reports 71.5% macro accuracy.

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