Community project / Evaluations and independent research
VideoJev
Apache-2.0 open System One video models that locally score temporal candidates and multiple-choice answers over a shared prefix without text generation; video inference requires no hosted API, and the models use their own task interface rather than a TypeSafe-compatible endpoint.
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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.
Apache-2.0 open-weight decision model, fine-tuned from ModernBERT-base, that answers Noul, Choice, Score, and multi-label questions with a probability for every option; after downloading the weights it can run inference locally in Python or a browser through ONNX, and its local /v1/systemone server works with TypeSafe's Python SDK via TYPESAFE_BASE_URL. It is English-only, and its author reports 61.2% accuracy on held-out datasets, with rating-scale answers weaker than the other types.
Apache-2.0 open-weight decision model for browser agents (a Qwen3.5-35B-A3B fine-tune) that answers Choice, Noul, and Score through a Jev-compatible /v1/systemone API and ships its training recipe, live-web training data, and a deterministic-verifier evaluation on 125 hand-picked real-website tasks. Its authors report 47/122 gradable successes versus 20/120 for Jev 1.13 inside the same agent, and 83.72% versus 84.70% mean accuracy on eight single-step typed-decision benchmarks; the live-web runs happened on different days and had different gradable subsets. It is a separate Jev-style alternative, not hosted Jev: self-hosting the weights needs one 80 GB GPU, and page observations go to the configured decision endpoint and text model.
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