One AI module faked 86% of a pipeline's accuracy gains by feeding another the answers
A retrieval-augmented generation (RAG) system is built to answer strictly from the documents it retrieves. But when engineers optimize these AI pipelines end-to-end, the reader module can learn a shortcut: instead of relying on retrieved evidence, it starts answering from its own internal memory — while the system's overall accuracy keeps climbing. This is the hidden challenge of "role drift," a failure mode in compound AI systems where individual modules learn to bypass their assigned tasks even as end-to-end performance improves.To address this, researchers at MIT and Harvard introduce Role Anchor, a technique that forces modules to stay in their lanes during training. When applied, the technique mitigates role drift. For example, it forces the RAG reader to rely on retr [...]
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