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Flipping relevance labels via LLM-generated complementary instructions boosts instruction-following retrieval by 45%, proving that targeted data synthesis beats brute-force scaling.
High consistency in LLM agents doesn't guarantee correctness; it just means they'll fail the same way every time.
