Cross-Pollination Retrieval: Finding Transferable Ideas Across Fields in Scientific Literature

Seokwon Jung; Yerang Kim; Yejin Kim; Alexander Rubinstein; Arnas Uselis; Sangwoo Park; Seong Joon Oh; Sangdoo Yun

cs.IROct 19, 2026

Cross-Pollination Retrieval: Finding Transferable Ideas Across Fields in Scientific Literature

Authors: Seokwon Jung, Yerang Kim, Yejin Kim, Alexander Rubinstein, Arnas Uselis, Sangwoo Park, Seong Joon Oh, Sangdoo Yun

Organizations: KAIST · Tübingen AI Center, University of Tübingen · NAVER AI Lab

Abstract

Many scientific advances come from cross-pollination, carrying an idea from one field over to a problem in another. Yet retrieving papers across fields remains underexplored, with existing benchmarks confined to a narrow range of fields. We study cross-pollination retrieval, the problem of identifying transferable ideas across research fields, and introduce CPR-Dataset, a large-scale dataset constructed by mining cross-topic citations. Its evaluation split CPR-eval has 1,300 queries in 26 fields, and its training split CPR-train has 10,573 pairs. On CPR-eval, the best of eleven existing retrievers reaches a Recall@10 of only 0.26, whereas an LLM reranker reaches 0.81. Standard contrastive fine-tuning on CPR-train raises Recall@10 to as high as 0.70. The gain carries over to domains held out from training and to analogy and reasoning-intensive retrieval. On AnaloBench, it raises nDCG@10 by up to 33% over the untrained retriever, whereas training on three existing retrieval datasets lowers it. On BRIGHT, it raises nDCG@10 by up to 70%, more than twice the gain from those datasets. CPR-Dataset is available at https://huggingface.co/CPR-Dataset, and the code at https://github.com/SeokwonJung-Jay/Cross-Pollination-Retrieval.

Figures & tables

Figure 1: Queries and their relevant papers stay in a narrow region of science in existing benchmarks, whereas those of our Cross-Pollination Retrieval benchmark span all four domains. Each panel shows the 100 pairs farthest apart on a PCA map of paper embeddings (E5-base-v2 ( Wang et al., 2024 ) ) in SciDocs ( Cohan et al., 2020 ) , LitSearch ( Ajith et al., 2024 ) , Same Problem, Different Field ( Kulikowski, 2026 ) , or ours, with background papers and ellipses colored by OpenAlex domain. Lines join each query (filled dot) to its relevant paper (open circle).

Figure 2: Example of a cross-pollination pair. The query describes the problem of Sohl-Dickstein et al. (2015) , which introduced diffusion models. A same-topic paper, generative adversarial nets ( Goodfellow et al., 2014 ) , cannot be transferred to it. A paper from nonequilibrium statistical physics ( Jarzynski, 1997 ) can: it moves a system from an initial to a final equilibrium state, as the base paper turns the data distribution into a tractable one. The query and titles are shortened.

Figure 3: Existing retrievers fail at cross-pollination retrieval, and training on CPR-train helps beyond CPR-eval. (a) Recall@10 on CPR-eval, averaged within each retriever family; the dotted line marks random ranking. Dense scientific and Dense reasoning denote retrievers trained for scientific literature search and for reasoning-intensive retrieval. (b) Relative gain of Qwen3-Embedding-4B over its untrained version after one epoch on the same number of pairs from each dataset (Recall@10 on CPR-eval, nDCG@10 on AnaloBench and BRIGHT); for existing datasets, the mean over three random samples. Colors are scaled within each row.

Figure 4: Construction pipeline of CPR-Dataset. Top: cited papers on other topics are filtered for analogy and transfer, and those rated useful for the generated query become positives (Section 3.2 ). Bottom: hard negatives are candidates pooled by three retrievers and rated not useful (Section 3.3 ).

Split	Queries	Base papers	Pos.	Neg.
CPR-eval	1,300	1,080	1	150
CPR-train	10,573	8,137	1	9

Table 1: Split sizes of CPR-Dataset. Number of queries and of the distinct base papers they come from, with positives (Pos.) and hard negatives (Neg.) per query.

	Domain
	Health		Life		Physical		Social		Avg.
Method	R@1	R@10	R@1	R@10	R@1	R@10	R@1	R@10	R@1	R@10
LLM reranker †
MiMo-V2.6-Pro	0.60	0.86	0.60	0.80	0.56	0.77	0.58	0.86	0.58	0.81
Sparse
BM25	0.03	0.16	0.04	0.16	0.04	0.16	0.07	0.19	0.04	0.16

Table 2: Recall@1 and Recall@10 of retrievers and an LLM reranker on CPR-eval. Columns show each OpenAlex domain of the base paper and all queries. Bold marks the best retriever per column. Random ranking gives 0.01 and 0.07. † For reference: the LLM reranker reorders the same 151 candidates in a tournament.

Figure 5: Retrievers trained on different data. Each retriever is evaluated before training and after training for one epoch on the same number of pairs (10,573), randomly sampled from each existing dataset; for the existing datasets, bars show the mean over three random samples and whiskers one standard deviation. Qwen3-0.6B and Qwen3-4B denote Qwen3-Embedding-0.6B and 4B, and Nemotron-8B denotes llama-embed-nemotron-8b. BRIGHT covers its StackExchange subsets, and AnaloBench averages stories of 1, 10, and 30 sentences.

Figure 6: Robustness of the difficulty of CPR-eval. (a) Recall@10 divided by its random expectation in the same pool: 10/151 with all candidates, and 10/101 after removing the 50 candidates that share an OpenAlex topic with the base paper. (b) Recall@10 on the original pools and on pools rebuilt with one of the three pooling retrievers left out. (c) Recall@10 of BM25 and Gemini Embedding with the original query and with queries rewritten by GLM-5.3; the dashed line marks the LLM reranker on all 151 candidates, and the dotted line marks random ranking.

Figure 7: Recall@10 of Qwen3-Embedding-4B on domains unseen in training. The retriever is evaluated on one OpenAlex domain before training, after training on CPR-train without that domain (5,896 pairs), and after training on all of CPR-train (10,573 pairs). Details and results for Qwen3-Embedding-0.6B are in Appendix L .

Appendix figures & tables19 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 8: An analogous pair mined by our pipeline.

q

: Solving the Latin Square Completion Problem by Memetic Graph Coloring.

d

: Precoloring Extension. I. Interval Graphs. Object mappings and shared relations are those extracted by the analogy judge (Appendix B.1 ), with object names shortened.

Query	Positive	Hard negative	Object mappings (query side $\mapsto$ positive)
How to define a representative conformation tensor average for turbulent viscoelastic flows that respects the non-Euclidean geometry of positive-definite tensors	A Differential Geometric Approach to the Geometric Mean of Symmetric Positive-Definite Matrices	Numerical Simulation of the Planar Extrudate Swell of Pseudoplastic and Viscoelastic Fluids	conformation tensors $\mapsto$ symmetric positive-definite matrices; geometric mean conformation tensor $\mapsto$ Riemannian geometric mean
How to understand the role of personal and social factors in the development of social entrepreneurship in Pakistan	Economic Action and Social Structure: The Problem of Embeddedness	Shaker A. Zahra: Pioneering Entrepreneurship Scholar	social entrepreneurs in Pakistan $\mapsto$ individuals undertaking economic action; social capital and networks $\mapsto$ structures of social relations
How to cost-effectively obtain large-scale sequence information for upland cotton flower development when the genome sequence is unavailable	An EST Resource for Tilapia Based on 17 Normalized Libraries	Improvement of Cotton Fiber Quality by Rabbit Keratin Gene Transformation	cotton normalized cDNA library $\mapsto$ tilapia normalized cDNA libraries; cotton EST reads $\mapsto$ tilapia EST reads
How to understand positive and negative responses to death portrayed in animated Disney films	The Development of Coping	Frozen in Time: How Disney Gender-Stereotypes Its Most Powerful Princess	film characters facing death $\mapsto$ children facing real-life stressors; coping portrayed in the films $\mapsto$ families of coping strategies

Appendix

Table 3: More analogous pairs mined by our pipeline. Each hard negative shares an OpenAlex topic with the base paper and was rated 1 (related but not usable) by the judge of Step N2. Two of the object mappings extracted by the analogy judge (Appendix B.1 ) are shown for each pair; queries, titles, and object names are shortened.

Task	Items	Agreement	Cohen’s $\kappa$
1. Transfer verification (P2)	50	0.92	0.84
2. Usefulness of positives (P3)	25	0.92	0.84
3. Usefulness of candidates (N2)	25	0.96	0.92

Appendix

Table 4: Agreement between the LLM judges and a human annotator. Usefulness scores are binarized at 2.

Domain	Field	Subfield	Topic
Health Sciences	Dentistry	Periodontics	Oral microbiology and periodontitis research
	Nursing	Nutrition and Dietetics	Child Nutrition and Water Access
Life Sciences	Biochemistry, Genetics and Molecular Biology	Genetics	Genetic Associations and Epidemiology
	Immunology and Microbiology	Immunology	Immunotherapy and Immune Responses
Physical Sciences	Computer Science	Computer Vision and Pattern Recognition	Advanced Neural Network Applications
	Mathematics	Statistics and Probability	Advanced Causal Inference Techniques

Appendix

Table 5: Examples of the OpenAlex hierarchy. Each row is a topic assigned to papers in CPR-eval, with the subfield, field, and domain it belongs to. OpenAlex has 4 domains, 26 fields, 252 subfields, and about 4,500 topics.

Table 14

Figure 9: All query and relevant paper pairs of each benchmark. The same map as Figure 1 , with every pair instead of the 100 with the longest lines.

	Domain
	Health		Life		Physical		Social		Avg.
Method	R@5	MRR	R@5	MRR	R@5	MRR	R@5	MRR	R@5	MRR
LLM reranker †
MiMo-V2.6-Pro	0.80	0.68	0.76	0.67	0.73	0.63	0.81	0.67	0.77	0.66
Sparse
BM25	0.09	0.06	0.12	0.07	0.12	0.07	0.12	0.10	0.11	0.07

Appendix

Table 8: Recall@5 and MRR@10 of retrievers and an LLM reranker on CPR-eval. Columns show results by the OpenAlex domain of the base paper and the average over all queries. The best retriever in each column is in bold. MRR denotes MRR@10. Random ranking gives 0.03 and 0.02. † Shown for reference: the LLM reranker reorders the same 151 candidates in a tournament.

Method	R@1	R@5	R@10	MRR@10
LLM reranker †
MiMo-V2.6-Pro	0.58 [0.55, 0.61]	0.77 [0.74, 0.79]	0.81 [0.79, 0.84]	0.66 [0.63, 0.68]
Sparse
BM25	0.04 [0.03, 0.05]	0.11 [0.10, 0.13]	0.16 [0.14, 0.19]	0.07 [0.06, 0.09]
BGE-M3 (sparse)	0.03 [0.02, 0.04]	0.08 [0.06, 0.09]	0.11 [0.09, 0.12]	0.05 [0.04, 0.06]
Dense (general)

Appendix

Table 9: Results on CPR-eval over all 1,300 queries with 95% confidence intervals. Brackets give the 2.5th and 97.5th percentiles over 2,000 bootstrap resamples of the queries. † Shown for reference: the LLM reranker reorders the same 151 candidates in a tournament.

Model	Training data	R@1	R@5	R@10	MRR@10
Qwen3-Emb-0.6B	Before training	0.04 [0.03, 0.05]	0.08 [0.06, 0.09]	0.12 [0.10, 0.14]	0.06 [0.05, 0.07]
	CPR-train	0.24 [0.21, 0.26]	0.41 [0.39, 0.44]	0.50 [0.47, 0.53]	0.31 [0.29, 0.33]
	MS MARCO	0.05 [0.03, 0.06]	0.09 [0.08, 0.11]	0.12 [0.11, 0.14]	0.07 [0.05, 0.08]
	ReasonIR data	0.03 [0.02, 0.04]	0.07 [0.06, 0.09]	0.11 [0.10, 0.13]	0.05 [0.04, 0.06]
	SPECTER data	0.02 [0.01, 0.03]	0.06 [0.05, 0.07]	0.09 [0.08, 0.11]	0.04 [0.03, 0.05]
Qwen3-Emb-4B	Before training	0.06 [0.04, 0.07]	0.13 [0.11, 0.15]	0.17 [0.15, 0.19]	0.09 [0.07, 0.10]

Appendix

Table 10: Retrievers trained on each training set, evaluated on CPR-eval over all 1,300 queries. Each model is trained for one epoch on 10,573 pairs with hard negatives and evaluated with a maximum length of 1,024 tokens. Brackets give 95% bootstrap confidence intervals. Bold marks the best value for each model, including the model before training. For MS MARCO, ReasonIR data, and SPECTER data, the values are from one sample (seed 42); Table 15 gives the means over three samples.

Model	Training data	Bio.	Earth	Econ.	Psy.	Rob.	Stack.	Sus.	Avg.
Qwen3-Emb-0.6B	Before training	0.113	0.211	0.158	0.136	0.111	0.120	0.116	0.138
	CPR-train	0.233	0.222	0.215	0.248	0.094	0.177	0.141	0.190
	MS MARCO	0.103	0.234	0.185	0.172	0.123	0.118	0.109	0.149
	ReasonIR data	0.129	0.130	0.148	0.114	0.050	0.147	0.083	0.114
	SPECTER data	0.131	0.192	0.183	0.170	0.098	0.187	0.107	0.153
Qwen3-Emb-4B	Before training	0.125	0.246	0.136	0.181	0.109	0.148	0.111	0.151

Appendix

Table 11: nDCG@10 on the seven StackExchange subsets of BRIGHT (all 749 queries; each subset is searched over its own corpus). Bio.: biology, Earth: earth science, Econ.: economics, Psy.: psychology, Rob.: robotics, Stack.: Stack Overflow, Sus.: sustainable living. Avg. is the mean over the seven subsets. Bold marks the best value for each model, including the model before training. For MS MARCO, ReasonIR data, and SPECTER data, the values are from one sample (seed 42); Table 15 gives the means over three samples.

		T2 nDCG@10				T1 accuracy
Model	Training data	S1	S10	S30	Avg.	S1	S10	S30	Avg.
Qwen3-Emb-0.6B	Before training	0.336	0.307	0.298	0.314	0.631	0.582	0.573	0.595
	CPR-train	0.450	0.393	0.364	0.402	0.686	0.644	0.627	0.652
	MS MARCO	0.369	0.264	0.259	0.297	0.653	0.541	0.546	0.580
	ReasonIR data	0.265	0.257	0.264	0.262	0.553	0.502	0.522	0.526
	SPECTER data	0.209	0.235	0.238	0.227	0.480	0.504	0.517	0.501

Appendix

Table 12: Results on AnaloBench. T2: retrieving analogous stories from a pool of stories (nDCG@10); T1: choosing the analogous story among four options (accuracy). S1, S10, and S30 are the story lengths in sentences; Avg. is their mean. Bold marks the best value for each model, including the model before training. For MS MARCO, ReasonIR data, and SPECTER data, the values are from one sample (seed 42); Table 15 gives the means over three samples.

	Qwen3-Embedding-0.6B / 4B	llama-embed-nemotron-8b
Framework	ms-swift, full fine-tuning	NeMo AutoModel 0.8.0, full fine-tuning
Batch size	128 queries (8 GPUs $\times$ 4 $\times$ 4 accumulation)	128 queries (8 GPUs $\times$ 1 $\times$ 16 accumulation)
Steps	83 (1 epoch)	83 (1 epoch)
Learning rate	$6\times 10^{-6}$ , cosine, no warmup	$2\times 10^{-6}$ , 2 warmup steps, cosine to 1%
Optimizer	AdamW ( $\beta$ = 0.9, 0.95), weight decay 0.1	AdamW ( $\beta$ = 0.9, 0.999), weight decay 0.01
Gradient clipping	1.0	1.0

Appendix

Table 13: Training hyperparameters. The same settings are used for every training set. Evaluation uses the same maximum length (1,024 tokens) and query prompt, for the trained models and for the models before training.

Training set	Source	Hard neg.
CPR-train	ours	9
MS MARCO	microsoft/ms_marco (v1.1, train)	9
ReasonIR data	reasonir/reasonir-data (HQ, VL)	1
SPECTER data	allenai/scirepeval ( cite_prediction )	1

Appendix

Table 14: Training sets. Each set has 10,573 pairs. Hard neg. is the number of hard negatives per pair.

Training set	CPR-eval	AnaloBench	BRIGHT
Qwen3-Embedding-0.6B
MS MARCO	.127 $\,\pm\,$ .004	.297 $\,\pm\,$ .001	.151 $\,\pm\,$ .006
ReasonIR data	.119 $\,\pm\,$ .004	.268 $\,\pm\,$ .010	.119 $\,\pm\,$ .005
SPECTER data	.092 $\,\pm\,$ .002	.228 $\,\pm\,$ .001	.154 $\,\pm\,$ .001
Qwen3-Embedding-4B
MS MARCO	.162 $\,\pm\,$ .005	.304 $\,\pm\,$ .008	.180 $\,\pm\,$ .008

Appendix

Table 15: Retrievers trained on three random samples of each comparison training set. Mean

\pm

sample standard deviation over seeds 42, 1, and 2, without leading zeros. CPR-eval: Recall@10 over all 1,300 queries. AnaloBench: T2 nDCG@10 averaged over S1, S10, and S30. BRIGHT: nDCG@10 averaged over the seven StackExchange subsets (all 749 queries).

Model	Training data	Health (250)	Life (250)	Physical (500)	Social (300)
Qwen3-Emb-0.6B	Before training	0.13 [0.09, 0.18]	0.10 [0.06, 0.14]	0.12 [0.09, 0.14]	0.13 [0.10, 0.17]
	Without the domain	0.47 [0.41, 0.54]	0.42 [0.36, 0.48]	0.33 [0.28, 0.37]	0.50 [0.44, 0.56]
	All domains	0.51 [0.45, 0.57]	0.48 [0.42, 0.54]	0.45 [0.40, 0.49]	0.60 [0.54, 0.66]
Qwen3-Emb-4B	Before training	0.18 [0.13, 0.22]	0.16 [0.12, 0.21]	0.16 [0.13, 0.19]	0.18 [0.14, 0.22]
	Without the domain	0.71 [0.65, 0.76]	0.62 [0.56, 0.67]	0.54 [0.49, 0.58]	0.72 [0.67, 0.77]
	All domains	0.68 [0.63, 0.74]	0.67 [0.61, 0.73]	0.66 [0.62, 0.70]	0.75 [0.70, 0.80]

Appendix

Table 17: Recall@10 on each held-out domain. Without the domain : trained on 5,896 pairs without the evaluated domain. All domains : trained on all 10,573 training pairs, including the evaluated domain. Brackets give 95% bootstrap confidence intervals; the number of queries is shown next to each domain.

		CPR-eval (R@10)		AnaloBench (nDCG@10)		BRIGHT (nDCG@10)
Model	Training data	HN	IB	HN	IB	HN	IB
Qwen3-Emb-0.6B	Before training	0.120		0.314		0.138
	CPR-train	0.501	0.323	0.402	0.330	0.190	0.176
	MS MARCO	0.124	0.093	0.297	0.224	0.149	0.131
	ReasonIR data	0.115	0.115	0.262	0.249	0.114	0.082
	SPECTER data	0.094	0.094	0.227	0.224	0.153	0.155

Appendix

Table 18: Retrievers trained with hard negatives (HN) and with in-batch negatives only (IB). CPR-eval: Recall@10 over all 1,300 queries. AnaloBench: T2 nDCG@10 averaged over S1, S10, and S30. BRIGHT: nDCG@10 averaged over the seven StackExchange subsets (all 749 queries). The same pairs are used in both settings; IB removes their hard negatives. For MS MARCO, ReasonIR data, and SPECTER data, the values are from one sample (seed 42).

Figure 11: Retrievers trained with in-batch negatives only. The same comparison as Figure 5 , but each retriever is trained without hard negatives. Before-training values are the same as in Figure 5 .

Query (shortened)	Relevant document	Idea that links them	Ranked first by the other retrievers	Rank
BRIGHT, earth science
Is wind just mainly nitrogen particles moving in one direction?	Wikipedia article on Brownian motion	a net drift on top of random thermal motion	Wikipedia article on wind; a page on air molecules hitting a moving car	1 / 636
Why were temperature anomalies negative between 1950 and 1980, although oil consumption had been massive since 1880?	Wikipedia article on global dimming	aerosols from air pollution blocked sunlight	Pages on temperature anomalies and on climate change	2 / 220
BRIGHT, robotics
In ROS2, do I need to rebuild the package after each change to a Python launch file?	A forum answer on symlink-install	linking the installed files to the source, so that edits take effect without compiling again	A page on the ros2 launch command; a tutorial that reruns colcon after adding a launch file	1 / >1000
AnaloBench, stories of 10 sentences

Appendix

Table 19: Queries solved only by the retriever trained on CPR-train. All retrievers are Qwen3-Embedding-4B. Rank : rank of the best relevant document for the retriever trained on CPR-train / the best of the same retriever trained on MS MARCO, ReasonIR data, or SPECTER data (one sample each, seed 42) or before training. BRIGHT ranks the documents of its subset; “>1000” means that the relevant document is not in the top 1,000. AnaloBench ranks a pool of 200 stories.

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RATIO: A Benchmark for Retrieval Across Typed Ideation Operations in Scientific Literature

Maayan Sharon, Tom Hope

The Hebrew University of Jerusalem · The Allen Institute for AI (AI2)

AI-Assisted Scientific Research IR Evaluation Scientific Document Retrieval

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Retrieved scientific literature can serve as inspiration for both human and AI scientists. Inspiration can take different forms: prior work may directly suggest how to address a problem, or surface directions at different levels of abstraction---zooming out to a more general view or zooming in to a concrete realization. We introduce RATIO (Retrieval Across Typed Ideation Operations), a large-scale benchmark in which relevance is defined by three operations which we name ideation moves: Address retrieves potential approaches for stated problems, Broaden retrieves more general formulations, and Specify retrieves concrete instantiations. RATIO is constructed from millions of full-text scientific papers across CS literature via a general recipe that extends discourse-marker distant supervision---previously used only for classification---to corpus-scale retrieval, combined with extensive LLM and human vetting. Experiments show that operation-specific fine-tuning substantially boosts retrievers and LLM reranking helps further, but much room for improvement remains. Researchers blind to condition found the fine-tuned retriever's candidates more useful as inspirations. RATIO provides a scalable training and evaluation framework for retrieval components that support literature-grounded ideation, opening up new research avenues in scientific inspiration retrieval. We release full data, code, models, and annotations.

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