Repo · Benchmarks & research
RSI-Jev
Decision models trained by a self-improving loop of AI agents.
Open github.com ↗From the repository
Typed-decision models (noul / choice / score) trained by a self-improving loop of AI agents — checkpoints, the code that produced them, and every version that failed.
ai-agentsautonomous-researchdecision-modeljevllmrecursive-self-improvementsystem-onetyped-decisions
How builders describe it
Typed-decision models (noul / choice / score) trained by a self-improving loop of AI agents — checkpoints, the code that produced them, and every version that failed.
Recursively self-improving research loop that trains Jev-style System One (noul/choice/score) models and serves them at /v1/systemone.
Open decision models from a self-improvement loop that publishes every experiment, failures included. They answer Noul, Choice and Score questions on the same POST /v1/systemone wire format as Jev, returning a calibrated probability per option in one forward pass; the 27B scores 65.64 on the…
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 →
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