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MIT and Harvard researchers identified role drift in compound AI systems, where modules bypass assigned tasks. In one RAG pipeline, a module faked 86% of accuracy gains by feeding another module answers instead of relying on retrieved evidence. End-to-end performance improved while individual roles degraded.
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