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In an experiment, the AI model Jev completes 100 trolley problems, choosing to sacrifice one person to save five with 99% probability in the first round.
A developer created a Jev-style API wrapper for the jina-reranker-v3.5 AI model to function as a decision engine, demonstrating its deterministic behavior in trolley problem scenarios and highlighting its training focus on retrieval relevance over rationality.