The authors show that LLMs suffer from mode collapse when generating solutions to open-ended scientific problems—they produce low-diversity outputs. They propose analogical reasoning (AR), which finds cross-domain problems with shared relational structure, generates analogies, and uses them to search for novel solutions. Compared to baselines, AR improves solution diversity metrics by 90–173%, generates novel solutions over 50% of the time (versus as low as 1.6% for baselines), and when implemented on four biomedical problems, yields consistent quantitative gains including state-of-the-art performance on oligonucleotide property prediction.
Main takeaways:
- LLMs mode-collapse into repetitive solutions on open-ended scientific problems, limiting their creative search.
- Analogical reasoning searches for cross-domain problems with similar relational structure, then maps solutions back to the target domain.
- Improves solution diversity by 90–173% and generates novel solutions over 50% of the time versus baselines.
- Validated on four real biomedical tasks: 13-fold improvement on perturbation effect prediction, outperforms baselines on cell-cell communication prediction, high correlation on brain region interaction inference, and state-of-the-art on two oligonucleotide property datasets.
- Demonstrates that prompting for analogies can augment the search space of existing solution-generation methods.