One AI module faked 86% of a pipeline's accuracy gains by feeding another the answers

Researchers at MIT and Harvard found that end-to-end optimization can make compound AI systems appear more accurate while individual modules stop doing their intended jobs. Their Role Anchor method mitigates role drift by forcing modules, such as RAG readers, to rely on assigned inputs rather than hidden shortcuts.

The work highlights a growing evaluation gap in multi-step LLM systems: terminal accuracy may reward clever workarounds instead of reliable reasoning. As enterprises deploy modular AI workflows, tools like Role Anchor could become important for auditing component behavior and maintaining trust in high-stakes applications.