Contested result

RNA-FM and SpliceBERT: Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications reverses the order

Low severity
Not yet reviewed
Ordering reversal
Different tasks across studies

Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications ranks SpliceBERT above RNA-FM in 3 of 3 comparisons; other studies rank RNA-FM above SpliceBERT in 31 of 36.

An automatic lead, not a finding of error. The results below are shown as published, everywhere on the site, until a reviewer decides otherwise.

Which studies agree

Zero-shot benchmarking of RNA language… ranks RNA-FM above SpliceBERT in 31 of its 36 comparisons and SpliceBERT higher in 5. Benchmarking pre-trained genomic… ranks SpliceBERT above RNA-FM in all 3 comparisons that are not ties (12 are ties).

Rank RNA-FM higher (1): Zero-shot benchmarking of RNA language…
Rank SpliceBERT higher (1): Benchmarking pre-trained genomic… (disagrees)
Mixed (0): none

Each paper sits where at least two thirds of its comparisons point; Mixed means neither model reaches two thirds. The larger, outlined dot is the paper that disagrees.

RNA-FM higherTieSpliceBERT higherNumbers are counts of comparisons; the outlined bar is the paper that disagrees.

Papers compared (2)
Paper	Role	Ranks RNA-FM higher	Ranks SpliceBERT higher	Ties
Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications and 1 more record · DOI	Disagrees	0	3	12
Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning and 1 more record · DOI	Agrees	31	5	0

Comparisons in Benchmarking pre-trained genomic… (15)

Protocol	Dataset	Metric	RNA-FM	SpliceBERT	Higher
GTEx tissue-specific splicing prediction, Splice acceptors and donors (You et al. 2026 Supplementary Table 5)	GTEx-derived tissue-specific splice site usage dataset (You et al. 2026)	AUPRC (GTEx splicing task, Splice acceptors and donors, averaged over categories), fraction	0.796	0.832	SpliceBERT
GTEx tissue-specific splicing prediction, Splice-15class (You et al. 2026 Supplementary Table 5)	GTEx-derived tissue-specific splice site usage dataset (You et al. 2026)	AUPRC (GTEx splicing task, Splice-15class, averaged over categories), fraction	0.677	0.754	SpliceBERT
GTEx tissue-specific splicing prediction, Splice-53class (You et al. 2026 Supplementary Table 5)	GTEx-derived tissue-specific splice site usage dataset (You et al. 2026)	AUPRC (GTEx splicing task, Splice-53class, averaged over categories), fraction	0.684	0.739	SpliceBERT
NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4)	Nucleotide Transformer benchmark splice-site dataset, acceptors	Accuracy (NT-splice-sites-acceptors, fine-tuned), fraction	0.98	0.987	Tie
NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4)	Nucleotide Transformer benchmark splice-site dataset, acceptors	F1 (NT-splice-sites-acceptors, fine-tuned), fraction	0.98	0.987	Tie
NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4)	Nucleotide Transformer benchmark splice-site dataset, acceptors	Precision (NT-splice-sites-acceptors, fine-tuned), fraction	0.98	0.987	Tie
NT-splice-sites-acceptors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4)	Nucleotide Transformer benchmark splice-site dataset, acceptors	Recall (NT-splice-sites-acceptors, fine-tuned), fraction	0.98	0.987	Tie
NT-splice-sites-all splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4)	Nucleotide Transformer benchmark splice-site dataset, all sites	Accuracy (NT-splice-sites-all, fine-tuned), fraction	0.981	0.986	Tie
NT-splice-sites-all splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4)	Nucleotide Transformer benchmark splice-site dataset, all sites	F1 (NT-splice-sites-all, fine-tuned), fraction	0.981	0.986	Tie
NT-splice-sites-all splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4)	Nucleotide Transformer benchmark splice-site dataset, all sites	Precision (NT-splice-sites-all, fine-tuned), fraction	0.981	0.986	Tie
NT-splice-sites-all splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4)	Nucleotide Transformer benchmark splice-site dataset, all sites	Recall (NT-splice-sites-all, fine-tuned), fraction	0.981	0.986	Tie
NT-splice-sites-donors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4)	Nucleotide Transformer benchmark splice-site dataset, donors	Accuracy (NT-splice-sites-donors, fine-tuned), fraction	0.984	0.992	Tie
NT-splice-sites-donors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4)	Nucleotide Transformer benchmark splice-site dataset, donors	F1 (NT-splice-sites-donors, fine-tuned), fraction	0.984	0.992	Tie
NT-splice-sites-donors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4)	Nucleotide Transformer benchmark splice-site dataset, donors	Precision (NT-splice-sites-donors, fine-tuned), fraction	0.984	0.992	Tie
NT-splice-sites-donors splice-site classification, fine-tuned models (You et al. 2026 Supplementary Table 4)	Nucleotide Transformer benchmark splice-site dataset, donors	Recall (NT-splice-sites-donors, fine-tuned), fraction	0.984	0.992	Tie

Comparisons in Zero-shot benchmarking of RNA language… (36)

Protocol	Dataset	Metric	RNA-FM	SpliceBERT	Higher
Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, AUC (mean over assays)	RNAGym ncRNA DMS assays (31 assays) (Wang et al. 2026)	AUROC (WT-LLR; mean over assays), unitless	0.609	0.631	SpliceBERT
Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, AUC (median over assays)	RNAGym ncRNA DMS assays (31 assays) (Wang et al. 2026)	AUROC (WT-LLR; median over assays), unitless	0.6	0.612	SpliceBERT
Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, MCC (mean over assays)	RNAGym ncRNA DMS assays (31 assays) (Wang et al. 2026)	MCC (WT-LLR; mean over assays), unitless	0.168	0.194	SpliceBERT
Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, MCC (median over assays)	RNAGym ncRNA DMS assays (31 assays) (Wang et al. 2026)	MCC (WT-LLR; median over assays), unitless	0.172	0.176	SpliceBERT
Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, SR (mean over assays)	RNAGym ncRNA DMS assays (31 assays) (Wang et al. 2026)	Absolute spearman correlation (WT-LLR; mean over assays), unitless	0.226	0.268	SpliceBERT
Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, SR (median over assays)	RNAGym ncRNA DMS assays (31 assays) (Wang et al. 2026)	Absolute spearman correlation (WT-LLR; median over assays), unitless	0.207	0.202	RNA-FM
Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, AUC (mean over assays)	RNAGym ncRNA DMS assays (31 assays) (Wang et al. 2026)	AUROC (PLL-D; mean over assays), unitless	0.569	0.55	RNA-FM
Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, AUC (median over assays)	RNAGym ncRNA DMS assays (31 assays) (Wang et al. 2026)	AUROC (PLL-D; median over assays), unitless	0.55	0.543	RNA-FM
Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, MCC (mean over assays)	RNAGym ncRNA DMS assays (31 assays) (Wang et al. 2026)	MCC (PLL-D; mean over assays), unitless	0.095	0.071	RNA-FM
Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, MCC (median over assays)	RNAGym ncRNA DMS assays (31 assays) (Wang et al. 2026)	MCC (PLL-D; median over assays), unitless	0.075	0.063	RNA-FM
Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, SR (mean over assays)	RNAGym ncRNA DMS assays (31 assays) (Wang et al. 2026)	Absolute spearman correlation (PLL-D; mean over assays), unitless	0.14	0.097	RNA-FM
Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, SR (median over assays)	RNAGym ncRNA DMS assays (31 assays) (Wang et al. 2026)	Absolute spearman correlation (PLL-D; median over assays), unitless	0.103	0.079	RNA-FM
Wang et al. 2026 zero-shot secondary structure, TS test set (70 PDB structures), attention, F1 score (mean over structures)	TS test set (70 PDB structures) (Wang et al. 2026)	F1 (attention; mean over structures), unitless	0.267	0.043	RNA-FM
Wang et al. 2026 zero-shot secondary structure, TS test set (70 PDB structures), attention, F1 score (median over structures)	TS test set (70 PDB structures) (Wang et al. 2026)	F1 (attention; median over structures), unitless	0.18	0.034	RNA-FM
Wang et al. 2026 zero-shot secondary structure, TS test set (70 PDB structures), attention, MCC (mean over structures)	TS test set (70 PDB structures) (Wang et al. 2026)	MCC (attention; mean over structures), unitless	0.273	0.033	RNA-FM
Wang et al. 2026 zero-shot secondary structure, TS test set (70 PDB structures), attention, MCC (median over structures)	TS test set (70 PDB structures) (Wang et al. 2026)	MCC (attention; median over structures), unitless	0.186	0.022	RNA-FM
Wang et al. 2026 zero-shot secondary structure, TS-Hard test set (15 PDB structures), attention, F1 score (mean over structures)	TS-Hard test set (15 PDB structures) (Wang et al. 2026)	F1 (attention; mean over structures), unitless	0.224	0.03	RNA-FM
Wang et al. 2026 zero-shot secondary structure, TS-Hard test set (15 PDB structures), attention, F1 score (median over structures)	TS-Hard test set (15 PDB structures) (Wang et al. 2026)	F1 (attention; median over structures), unitless	0.148	0.034	RNA-FM
Wang et al. 2026 zero-shot secondary structure, TS-Hard test set (15 PDB structures), attention, MCC (mean over structures)	TS-Hard test set (15 PDB structures) (Wang et al. 2026)	MCC (attention; mean over structures), unitless	0.231	0.02	RNA-FM
Wang et al. 2026 zero-shot secondary structure, TS-Hard test set (15 PDB structures), attention, MCC (median over structures)	TS-Hard test set (15 PDB structures) (Wang et al. 2026)	MCC (attention; median over structures), unitless	0.164	0.025	RNA-FM
Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), FFT embedding, 1 − OR (mean over repeated random samplings of sequence pairs)	RfamSample (561 Rfam families) (Wang et al. 2026)	One minus overlap ratio (FFT embedding; mean over random samplings of sequence pairs), unitless	0.878	0.567	RNA-FM
Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), FFT embedding, AUC (mean over repeated random samplings of sequence pairs)	RfamSample (561 Rfam families) (Wang et al. 2026)	AUROC (FFT embedding; mean over random samplings of sequence pairs), unitless	0.953	0.762	RNA-FM
Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), FFT embedding, F1 (mean over repeated random samplings of sequence pairs)	RfamSample (561 Rfam families) (Wang et al. 2026)	F1 (FFT embedding; mean over random samplings of sequence pairs), unitless	0.889	0.706	RNA-FM
Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), FFT embedding, MCC (mean over repeated random samplings of sequence pairs)	RfamSample (561 Rfam families) (Wang et al. 2026)	MCC (FFT embedding; mean over random samplings of sequence pairs), unitless	0.771	0.341	RNA-FM
Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), FFT embedding, 1 − OR (mean over repeated random samplings of sequence pairs)	ArchiveII-Nr (1273 RNAs) (Wang et al. 2026)	One minus overlap ratio (FFT embedding; mean over random samplings of sequence pairs), unitless	0.75	0.487	RNA-FM
Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), FFT embedding, AUC (mean over repeated random samplings of sequence pairs)	ArchiveII-Nr (1273 RNAs) (Wang et al. 2026)	AUROC (FFT embedding; mean over random samplings of sequence pairs), unitless	0.872	0.731	RNA-FM
Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), FFT embedding, F1 (mean over repeated random samplings of sequence pairs)	ArchiveII-Nr (1273 RNAs) (Wang et al. 2026)	F1 (FFT embedding; mean over random samplings of sequence pairs), unitless	0.789	0.712	RNA-FM
Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), FFT embedding, MCC (mean over repeated random samplings of sequence pairs)	ArchiveII-Nr (1273 RNAs) (Wang et al. 2026)	MCC (FFT embedding; mean over random samplings of sequence pairs), unitless	0.568	0.312	RNA-FM
Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), CLS embedding, 1 − OR (mean over repeated random samplings of sequence pairs)	RfamSample (561 Rfam families) (Wang et al. 2026)	One minus overlap ratio (CLS embedding; mean over random samplings of sequence pairs), unitless	0.891	0.633	RNA-FM
Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), CLS embedding, AUC (mean over repeated random samplings of sequence pairs)	RfamSample (561 Rfam families) (Wang et al. 2026)	AUROC (CLS embedding; mean over random samplings of sequence pairs), unitless	0.93	0.804	RNA-FM
Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), CLS embedding, F1 (mean over repeated random samplings of sequence pairs)	RfamSample (561 Rfam families) (Wang et al. 2026)	F1 (CLS embedding; mean over random samplings of sequence pairs), unitless	0.898	0.735	RNA-FM
Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), CLS embedding, MCC (mean over repeated random samplings of sequence pairs)	RfamSample (561 Rfam families) (Wang et al. 2026)	MCC (CLS embedding; mean over random samplings of sequence pairs), unitless	0.803	0.426	RNA-FM
Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), CLS embedding, 1 − OR (mean over repeated random samplings of sequence pairs)	ArchiveII-Nr (1273 RNAs) (Wang et al. 2026)	One minus overlap ratio (CLS embedding; mean over random samplings of sequence pairs), unitless	0.771	0.469	RNA-FM
Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), CLS embedding, AUC (mean over repeated random samplings of sequence pairs)	ArchiveII-Nr (1273 RNAs) (Wang et al. 2026)	AUROC (CLS embedding; mean over random samplings of sequence pairs), unitless	0.861	0.727	RNA-FM
Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), CLS embedding, F1 (mean over repeated random samplings of sequence pairs)	ArchiveII-Nr (1273 RNAs) (Wang et al. 2026)	F1 (CLS embedding; mean over random samplings of sequence pairs), unitless	0.803	0.709	RNA-FM
Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), CLS embedding, MCC (mean over repeated random samplings of sequence pairs)	ArchiveII-Nr (1273 RNAs) (Wang et al. 2026)	MCC (CLS embedding; mean over random samplings of sequence pairs), unitless	0.635	0.308	RNA-FM

What disagrees

Other studies: rank RNA-FM above SpliceBERT in 31 of 36 comparisons
This study: ranks SpliceBERT above RNA-FM in 3 of 3 comparisons

Compared: RNA-FM, SpliceBERT
Study that disagrees: Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications, You et al. 2026, Supplementary Information
Other studies: Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning, Zero-shot benchmarking of RNA language models: Supplementary Information
Rule: Ordering reversal; different tasks across studies
Model identity: Matched across studies partly through links not yet reviewed

The compared numbers

Each value links to its result record and source. Compare values within one protocol; the rule compares rankings, not raw values across protocols.

In the study that disagrees (6)
Study	Tool	Protocol or benchmark	Metric	Value
Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information	RNAFM fine-tuned with an appended convolutional layer (You et al. 2026)	GTEx tissue-specific splicing prediction, Splice acceptors and donors (You et al. 2026 Supplementary Table 5)	AUPRC (GTEx splicing task, Splice acceptors and donors, averaged over categories)	0.7959(0.0011)
Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information	SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)	GTEx tissue-specific splicing prediction, Splice acceptors and donors (You et al. 2026 Supplementary Table 5)	AUPRC (GTEx splicing task, Splice acceptors and donors, averaged over categories)	0.8317(0.0096)
Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information	RNAFM fine-tuned with an appended convolutional layer (You et al. 2026)	GTEx tissue-specific splicing prediction, Splice-15class (You et al. 2026 Supplementary Table 5)	AUPRC (GTEx splicing task, Splice-15class, averaged over categories)	0.6770(0.0033)
Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information	SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)	GTEx tissue-specific splicing prediction, Splice-15class (You et al. 2026 Supplementary Table 5)	AUPRC (GTEx splicing task, Splice-15class, averaged over categories)	0.7538(0.0014)
Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information	RNAFM fine-tuned with an appended convolutional layer (You et al. 2026)	GTEx tissue-specific splicing prediction, Splice-53class (You et al. 2026 Supplementary Table 5)	AUPRC (GTEx splicing task, Splice-53class, averaged over categories)	0.6844(0.0035)
Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications; You et al. 2026, Supplementary Information	SpliceBERT fine-tuned with an appended convolutional layer (You et al. 2026)	GTEx tissue-specific splicing prediction, Splice-53class (You et al. 2026 Supplementary Table 5)	AUPRC (GTEx splicing task, Splice-53class, averaged over categories)	0.7387(0.0020)

In the other studies (72 results)

In the other studies
Study	Tool	Protocol or benchmark	Metric	Value
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, AUC (mean over assays)	AUROC (WT-LLR; mean over assays)	0.609±0.081
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, AUC (median over assays)	AUROC (WT-LLR; median over assays)	0.6
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, MCC (mean over assays)	MCC (WT-LLR; mean over assays)	0.168±0.125
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, MCC (median over assays)	MCC (WT-LLR; median over assays)	0.172
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, SR (mean over assays)	Absolute spearman correlation (WT-LLR; mean over assays)	0.226±0.170
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, SR (median over assays)	Absolute spearman correlation (WT-LLR; median over assays)	0.207
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, AUC (mean over assays)	AUROC (PLL-D; mean over assays)	0.569±0.057
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, AUC (median over assays)	AUROC (PLL-D; median over assays)	0.55
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, MCC (mean over assays)	MCC (PLL-D; mean over assays)	0.095±0.085
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, MCC (median over assays)	MCC (PLL-D; median over assays)	0.075
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, SR (mean over assays)	Absolute spearman correlation (PLL-D; mean over assays)	0.140±0.116
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, SR (median over assays)	Absolute spearman correlation (PLL-D; median over assays)	0.103
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot secondary structure, TS test set (70 PDB structures), attention, F1 score (mean over structures)	F1 (attention; mean over structures)	0.267±0.239
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot secondary structure, TS test set (70 PDB structures), attention, F1 score (median over structures)	F1 (attention; median over structures)	0.18
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot secondary structure, TS test set (70 PDB structures), attention, MCC (mean over structures)	MCC (attention; mean over structures)	0.273±0.239
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot secondary structure, TS test set (70 PDB structures), attention, MCC (median over structures)	MCC (attention; median over structures)	0.186
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot secondary structure, TS-Hard test set (15 PDB structures), attention, F1 score (mean over structures)	F1 (attention; mean over structures)	0.224±0.228
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot secondary structure, TS-Hard test set (15 PDB structures), attention, F1 score (median over structures)	F1 (attention; median over structures)	0.148
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot secondary structure, TS-Hard test set (15 PDB structures), attention, MCC (mean over structures)	MCC (attention; mean over structures)	0.231±0.228
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot secondary structure, TS-Hard test set (15 PDB structures), attention, MCC (median over structures)	MCC (attention; median over structures)	0.164
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), FFT embedding, 1 − OR (mean over repeated random samplings of sequence pairs)	One minus overlap ratio (FFT embedding; mean over random samplings of sequence pairs)	0.878±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), FFT embedding, AUC (mean over repeated random samplings of sequence pairs)	AUROC (FFT embedding; mean over random samplings of sequence pairs)	0.953±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), FFT embedding, F1 (mean over repeated random samplings of sequence pairs)	F1 (FFT embedding; mean over random samplings of sequence pairs)	0.889±0.000
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), FFT embedding, MCC (mean over repeated random samplings of sequence pairs)	MCC (FFT embedding; mean over random samplings of sequence pairs)	0.771±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), FFT embedding, 1 − OR (mean over repeated random samplings of sequence pairs)	One minus overlap ratio (FFT embedding; mean over random samplings of sequence pairs)	0.750±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), FFT embedding, AUC (mean over repeated random samplings of sequence pairs)	AUROC (FFT embedding; mean over random samplings of sequence pairs)	0.872±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), FFT embedding, F1 (mean over repeated random samplings of sequence pairs)	F1 (FFT embedding; mean over random samplings of sequence pairs)	0.789±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), FFT embedding, MCC (mean over repeated random samplings of sequence pairs)	MCC (FFT embedding; mean over random samplings of sequence pairs)	0.568±0.002
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), CLS embedding, 1 − OR (mean over repeated random samplings of sequence pairs)	One minus overlap ratio (CLS embedding; mean over random samplings of sequence pairs)	0.891±0.000
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), CLS embedding, AUC (mean over repeated random samplings of sequence pairs)	AUROC (CLS embedding; mean over random samplings of sequence pairs)	0.930±0.000
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), CLS embedding, F1 (mean over repeated random samplings of sequence pairs)	F1 (CLS embedding; mean over random samplings of sequence pairs)	0.898±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), CLS embedding, MCC (mean over repeated random samplings of sequence pairs)	MCC (CLS embedding; mean over random samplings of sequence pairs)	0.803±0.000
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), CLS embedding, 1 − OR (mean over repeated random samplings of sequence pairs)	One minus overlap ratio (CLS embedding; mean over random samplings of sequence pairs)	0.771±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), CLS embedding, AUC (mean over repeated random samplings of sequence pairs)	AUROC (CLS embedding; mean over random samplings of sequence pairs)	0.861±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), CLS embedding, F1 (mean over repeated random samplings of sequence pairs)	F1 (CLS embedding; mean over random samplings of sequence pairs)	0.803±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	RNA-FM (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), CLS embedding, MCC (mean over repeated random samplings of sequence pairs)	MCC (CLS embedding; mean over random samplings of sequence pairs)	0.635±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, AUC (mean over assays)	AUROC (WT-LLR; mean over assays)	0.631±0.074
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, AUC (median over assays)	AUROC (WT-LLR; median over assays)	0.612
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, MCC (mean over assays)	MCC (WT-LLR; mean over assays)	0.194±0.109
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, MCC (median over assays)	MCC (WT-LLR; median over assays)	0.176
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, SR (mean over assays)	Absolute spearman correlation (WT-LLR; mean over assays)	0.268±0.149
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), WT-LLR, SR (median over assays)	Absolute spearman correlation (WT-LLR; median over assays)	0.202
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, AUC (mean over assays)	AUROC (PLL-D; mean over assays)	0.550±0.044
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, AUC (median over assays)	AUROC (PLL-D; median over assays)	0.543
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, MCC (mean over assays)	MCC (PLL-D; mean over assays)	0.071±0.059
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, MCC (median over assays)	MCC (PLL-D; median over assays)	0.063
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, SR (mean over assays)	Absolute spearman correlation (PLL-D; mean over assays)	0.097±0.088
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot fitness, RNAGym ncRNA DMS assays (31 assays), PLL-D, SR (median over assays)	Absolute spearman correlation (PLL-D; median over assays)	0.079
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot secondary structure, TS test set (70 PDB structures), attention, F1 score (mean over structures)	F1 (attention; mean over structures)	0.043±0.039
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot secondary structure, TS test set (70 PDB structures), attention, F1 score (median over structures)	F1 (attention; median over structures)	0.034
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot secondary structure, TS test set (70 PDB structures), attention, MCC (mean over structures)	MCC (attention; mean over structures)	0.033±0.045
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot secondary structure, TS test set (70 PDB structures), attention, MCC (median over structures)	MCC (attention; median over structures)	0.022
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot secondary structure, TS-Hard test set (15 PDB structures), attention, F1 score (mean over structures)	F1 (attention; mean over structures)	0.030±0.023
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot secondary structure, TS-Hard test set (15 PDB structures), attention, F1 score (median over structures)	F1 (attention; median over structures)	0.034
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot secondary structure, TS-Hard test set (15 PDB structures), attention, MCC (mean over structures)	MCC (attention; mean over structures)	0.020±0.026
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot secondary structure, TS-Hard test set (15 PDB structures), attention, MCC (median over structures)	MCC (attention; median over structures)	0.025
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), FFT embedding, 1 − OR (mean over repeated random samplings of sequence pairs)	One minus overlap ratio (FFT embedding; mean over random samplings of sequence pairs)	0.567±0.002
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), FFT embedding, AUC (mean over repeated random samplings of sequence pairs)	AUROC (FFT embedding; mean over random samplings of sequence pairs)	0.762±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), FFT embedding, F1 (mean over repeated random samplings of sequence pairs)	F1 (FFT embedding; mean over random samplings of sequence pairs)	0.706±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), FFT embedding, MCC (mean over repeated random samplings of sequence pairs)	MCC (FFT embedding; mean over random samplings of sequence pairs)	0.341±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), FFT embedding, 1 − OR (mean over repeated random samplings of sequence pairs)	One minus overlap ratio (FFT embedding; mean over random samplings of sequence pairs)	0.487±0.003
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), FFT embedding, AUC (mean over repeated random samplings of sequence pairs)	AUROC (FFT embedding; mean over random samplings of sequence pairs)	0.731±0.002
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), FFT embedding, F1 (mean over repeated random samplings of sequence pairs)	F1 (FFT embedding; mean over random samplings of sequence pairs)	0.712±0.000
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), FFT embedding, MCC (mean over repeated random samplings of sequence pairs)	MCC (FFT embedding; mean over random samplings of sequence pairs)	0.312±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), CLS embedding, 1 − OR (mean over repeated random samplings of sequence pairs)	One minus overlap ratio (CLS embedding; mean over random samplings of sequence pairs)	0.633±0.002
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), CLS embedding, AUC (mean over repeated random samplings of sequence pairs)	AUROC (CLS embedding; mean over random samplings of sequence pairs)	0.804±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), CLS embedding, F1 (mean over repeated random samplings of sequence pairs)	F1 (CLS embedding; mean over random samplings of sequence pairs)	0.735±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot family classification, RfamSample (561 Rfam families), CLS embedding, MCC (mean over repeated random samplings of sequence pairs)	MCC (CLS embedding; mean over random samplings of sequence pairs)	0.426±0.002
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), CLS embedding, 1 − OR (mean over repeated random samplings of sequence pairs)	One minus overlap ratio (CLS embedding; mean over random samplings of sequence pairs)	0.469±0.002
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), CLS embedding, AUC (mean over repeated random samplings of sequence pairs)	AUROC (CLS embedding; mean over random samplings of sequence pairs)	0.727±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), CLS embedding, F1 (mean over repeated random samplings of sequence pairs)	F1 (CLS embedding; mean over random samplings of sequence pairs)	0.709±0.001
Zero-shot benchmarking of RNA language models: Supplementary Information; Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning	SpliceBERT (zero-shot, Wang et al. 2026)	Wang et al. 2026 zero-shot RNA type classification, ArchiveII-Nr (1273 RNAs), CLS embedding, MCC (mean over repeated random samplings of sequence pairs)	MCC (CLS embedding; mean over random samplings of sequence pairs)	0.308±0.002

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