QD-Rec: Open-Ended Evolution of Recommendations via Quality–Diversity Blog: pub.sakana.ai/qd-rec GitHub: github.com/SakanaAI/QD-Rec arXiv: arxiv.org/abs/2610.23470 Open-ended search has been used to discover algorithms, generate scientific ideas, and develop self-improving agents. With QD-Rec, we explore how it can support a different kind of task: helping people make decisions. Which company should an acquirer buy? Which customers might benefit from a product? Which papers should a researcher read? In each case, the task is to choose among existing options, not generate new ones. At first glance, a finite, predefined candidate pool seems to leave little room for open-ended exploration. However, a fixed set of options does not imply a fixed set of reasons for choosing them. The same company might be worth considering for its technology or its potential to open up a new market. The search over these reasons remains open-ended even when the options themselves are fixed. QD-Rec searches over both what to recommend and why. Instead of aiming for a single best answer, it builds a diverse collection of recommendations, each pairing a candidate with a natural-language hypothesis explaining why it fits a particular decision maker’s goals, interests, and background. The system combines three components. A contextual bandit learns which items to investigate next, balancing promising candidates with less-explored ones. An LLM generates hypotheses by adapting or combining the earlier recommendations. An archive retains new recommendations when they offer a novel reason, or replaces a similar recommendation when the new one is judged better. We evaluate QD-Rec on acquisition target discovery, customer targeting, and paper recommendation. Compared with variants that remove evolutionary hypothesis generation or contextual bandits, QD-Rec achieves the highest mean LLM-rated quality in most settings and usually builds the largest archive. We also found examples where earlier recommendations with a low quality became stepping stones for later discoveries. These experiments are a starting point. Practical applications will require richer, up-to-date information and expert human feedback to assess whether the proposed QD-Rec holds up beyond evaluation by an LLM judge. We are releasing the code for QD-Rec so that researchers and practitioners can explore open-ended recommendation with their own decision-making tasks.