QD-Rec Applies Open-Ended Evolution to Recommendation Tasks
The shift here is from optimising for one best answer to building an archive of diverse, reasoned recommendations — useful when a decision maker needs options, not rankings. Practitioners running discovery-style tasks such as M&A screening or research triage can reuse the released code with their own candidate pools. The authors note limits: results rest on LLM judges, and real deployments would need fresh data and human expert feedback.
Sakana AI released QD-Rec, a system that applies open-ended quality–diversity search to recommendation tasks. It combines a contextual bandit for selecting which candidates to explore, an LLM that generates natural-language hypotheses explaining each recommendation, and an archive that keeps novel or better recommendations. The team evaluated it on three tasks: acquisition target discovery, customer targeting, and paper recommendation. Compared with ablated variants lacking evolutionary hypothesis generation or the bandit, QD-Rec achieved the highest mean LLM-rated quality in most settings. Code is available on GitHub alongside a blog post and arXiv paper.
QD-Rec: Open-Ended Evolution of Recommendations via Quality–Diversity
Blog: https://t.co/UEkTkeGJY1 GitHub: https://t.co/6ER0m7XVRq arXiv: https://t.co/zg1gtm0OlK
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.
