Jev matched 72.5% of Kepler labels after a prompt rewrite
Pavel Rabtsevich's linked test classified 8,054 historical signals, but its stronger result came after the first run informed a redesigned prompt.
By Ryan Merket · Published
Primary source: X
Why it matters
The 1,999 figure is a class-specific recall result from a retrospective label-prediction test. Jev beat the fixed rules baseline only after a prompt redesign informed by an earlier run, so the result shows how much task framing can move an AI benchmark - and why it should not be described as discovering planets.
Pavel Rabtsevich said on September 23rd that he had used Jev to classify NASA Kepler signals, reporting 1,999 correct matches among 2,731 archive-labeled confirmed planets. The technical report linked with the experiment puts that result in context: it was the second of two runs, after the first attempt scored below a simple rules-based benchmark.…
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