AI search tools now skip redundant results — by measuring diversity of information instead of just relevance

When AI language models retrieve background information to answer questions, they traditionally pick the most relevant chunks of text, which often means picking similar or overlapping sources that repeat the same point. A new method (ScalDPP) adds a second filter that selects for diversity — ensuring the AI grabs sources that complement each other and cover more ground. This means AI search gets smarter about what information actually matters for answering questions, not just what looks most relevant on its own.

Current AI search wastes retrieval slots on duplicative information — if five sources all say the same thing, the AI has one answer repeated five times instead of five different angles. This method treats retrieval as a structural problem: dependencies between sources matter as much as individual relevance scores. The real effect is cleaner answers with fewer hallucinations, since the AI is grounded in complementary evidence rather than circular confirmation.