Recombination is a common form of ideation that involves breaking down and blending existing ideas across domains to create novel solutions.
Re-what?
A hallmark of human innovation is recombination -- the creation of novel ideas by integrating elements from existing concepts and mechanisms. In this work, we introduce CHIMERA, the first large-scale Knowledge Base (KB) of recombination examples automatically mined from the scientific literature. CHIMERA enables empirical analysis of how scientists recombine concepts and draw inspiration from different areas, and enables training models that propose cross-disciplinary research directions. To construct this KB, we define a new information extraction task: identifying recombination instances in papers. We curate an expert-annotated dataset and use it to fine-tune an LLM-based extraction model, which we apply to a broad corpus of AI papers. We also demonstrate generalization to a biological domain. We showcase the utility of CHIMERA through two applications. First, we analyze patterns of recombination across AI subfields. Second, we train a scientific hypothesis generation model using the KB, showing that it can propose directions that researchers rate as inspiring.
Recombination is a common form of ideation that involves breaking down and blending existing ideas across domains to create novel solutions.
We automatically mine CHIMERA, a knowledge base of recombination examples from across the scientific literature.
Our approach to mining recombination examples begins with building a curated dataset of annotated examples. We then use this dataset to train an information extraction model. Finally, we apply the trained model on arXiv to collect recombination examples at scale.
We focus on two recombination types, which we name blends and inspirations. Blends combine multiple concepts to create new approaches (e.g., boosting classical machine learning algorithms using quantum computing), while inspirations involve adaption of ideas from existing concepts to spark insight (e.g., applying bird flock behavior to coordinate drone swarms).
CHIMERA includes over 28K examples of idea recombination, spanning both conceptual blends within and across domains, as well as inspiration-driven links such as analogies, reductions, and abstractions.
Inspirational connections are often cross-domain. Of note is the volume of inspiration drawn from brain-related sources, such as cognitive science and q-bio.nc. A possible explanation might be that many of our arXiv categories of interest are related to machine learning, where the human brain historically serves as a general source of inspiration.
We observe that while some sources of inspiration (like cognitive-science) are commonly shared across related fields, domains may draw inspiration from unique sources (e.g., from zoology to cs.RO).
Blends often connect the same or similar domains.
Using CHIMERA, we train supervised models that learn how to recombine concepts for predicting new scientific ideas.
We experiment with retrievers based on encoders trained prior to the test set cutoff year (2024), and find that fine-tuning them on our data improves the median rank of the gold answer (MedR) by an order of magnitude.
We invited researchers with proven experience—specifically, authors of at least one published paper—to evaluate the suggestions generated by our recombination prediction model against various baselines.
Researchers rated our recombination suggestions as nearly as helpful as the gold answers in inspiring new ideas, providing additional validation for our automated evaluation metrics.
@misc{sternlicht2026chimeraknowledgebasescientific,
title={CHIMERA: A Knowledge Base of Scientific Idea Recombinations for Research Analysis and Ideation},
author={Noy Sternlicht and Tom Hope},
year={2026},
eprint={2505.20779},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.20779},
}