Scientific AI requires breaking high-probability trajectory dependency and building real-world verification loops
manoelribeiro · x · 2026-08-25
The commenter notes that in scientific AI, the distinction matters because the most valuable outcomes are often not high-probability trajectories already in the data. This drives interest in closing the loop between AI and real-world scientific verification, moving beyond learning modes to allowing models to experiment.
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