Sakana AI's QD-Rec Applies Quality-Diversity Evolutionary Search to Open-Ended Recommendation
SakanaAILabs · x · 2026-10-20
Sakana AI released QD-Rec, a new recommendation method with blog post, paper, and open-source code.
- Problem: Decisions like M&A target screening, customer targeting, and paper recommendation need multiple perspectives on "why pick this and what's possible," not just score ranking. With candidates numbering in the thousands to 10,000+, exhaustively generating hypotheses is too costly — Sakana frames this as "open-ended recommendation."
- Method: Applies Quality-Diversity (QD) evolutionary search from open-ended exploration research: an LLM transfers accumulated hypotheses to new candidates and combines multiple hypotheses to generate new proposals; diverse proposals are kept while similar ones are replaced by better versions, using hypotheses as stepping stones.
- Experiments: Tested on M&A candidate exploration, targeting with synthetic customer profiles, and researcher paper recommendation. Under equal evaluation budgets, QD-Rec scored higher and accumulated more diverse recommendations than baselines in most settings.
- Positioning: AI should not replace human decisions but supply diverse promising options and comparison material; Sakana aims to develop QD-Rec into foundation technology for next-generation decision-making.
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