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ByteRover's memory-based retrieval achieved 91.3% F1 accuracy compared to Cursor's 60% manual file tagging in a Next.js codebase benchmark. The automated approach excelled at cross-module features by encoding architectural relationships during curation, eliminating noise from test utilities and legacy code. Manual tagging struggled when implementations spanned multiple files, often retrieving adjacent infrastructure instead of core logic. ByteRover also reduced redundant context fetching by 50% across related queries by reusing cached architectural knowledge. The precision advantage translated to 100% task completion versus 90% for manual selection.