Unlocking LLM Creativity in Science through Analogical Reasoning

Authors: Andrew Shen, Shaul Druckmann, James Zou

arXiv · PDF

Summary

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.

Relevance

Tangential—relevant only if I consider analogical reasoning as a behavioral installation method. The idea of steering model outputs via structured prompts (analogies as relational templates) loosely connects to my work on finding prompts corresponding to specific behaviors.

Abstract

arXiv:2605.11258v1 Announce Type: new Abstract: Autonomous science promises to augment scientific discovery, particularly in complex fields like biomedicine. However, this requires AI systems that can consistently generate novel and diverse solutions to open-ended problems. We evaluate LLMs on the task of open-ended solution generation and quantify their tendency to mode collapse into low-diversity generations. To mitigate this mode collapse, we introduce analogical reasoning (AR) as a new approach to solution generation. AR generates analogies to cross-domain problems based on shared relational structure, then uses those analogies to search for novel solutions. Compared to baselines, AR discovers significantly more diverse generations (improving solution diversity metrics by 90-173%), generates novel solutions over 50% of the time (compared to as little as 1.6% for baselines), and produces high-quality analogies. To validate the real-world feasibility of AR, we implement AR-generated solutions across four biomedical problems, yielding consistent quantitative gains. AR-generated approaches achieve a nearly 13-fold improvement on distributional metrics for perturbation effect prediction, outperform all baselines on AUPRC when predicting cell-cell communication, infer brain region interactions with a high Spearman correlation ($\rho$=0.729) to published methods, and establish state-of-the-art performance on 2 datasets for oligonucleotide property prediction. The novel and diverse solutions produced by AR can be used to augment the search space of existing solution generation methods.