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Paper Citation Record · LEDGER

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations

As of 9 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2508.13191.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2508.13191 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T20:00:19.244272Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ef6ad573-0e0e-4d95-9c32-9d31fe4a8b51 · outbound

This paper cites Accurate structure prediction of biomolecular interactions with alphafold 3.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Accurate structure prediction of biomolecular interactions with alphafold 3

Reference 1

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Observation a538efe2-0957-4cbf-bc65-909ee3b22040 · outbound

This paper cites Predicting the sequence specificities of dna-and rna-binding proteins by deep learning.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Predicting the sequence specificities of dna-and rna-binding proteins by deep learning

Reference 2

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 621ddb4e-d95d-4984-b73c-76dcf10f4bd0 · outbound

This paper cites Effective gene expression prediction from sequence by integrating long-range interactions.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Effective gene expression prediction from sequence by integrating long-range interactions

Reference 3

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Observation 5a9650d0-9909-4e95-a17b-bae4f1b9ff82 · outbound

This paper cites Longformer: The Long-Document Transformer.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Longformer: The Long-Document Transformer

Reference 4

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Observation 5da97bcc-5848-4c48-a849-767d2e25fe61 · outbound

This paper cites ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Reference 5

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Observation 85d7f9d2-b689-4a21-8e7b-2a441243d296 · outbound

This paper cites Nucleotide transformer: building and evaluating robust foundation models for human genomics.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Nucleotide transformer: building and evaluating robust foundation models for human genomics

Reference 6

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Source-reported events for the cited work

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Observation 34cd5e14-21ab-4aa4-bfb1-a7907df7f1eb · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 7

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Observation e71c6be4-6313-41e9-a312-d33879170725 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 8

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Observation 2aaa8ca1-1b8f-44b9-92e8-40a81806381b · outbound

This paper cites Effect of tokenization on transformers for biological sequences.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Effect of tokenization on transformers for biological sequences

Reference 9

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verified fuzzy
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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 96131c80-5ef1-44c9-98f4-03167a6a5343 · outbound

This paper cites Genomic benchmarks: a collection of datasets for genomic sequence classification.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Genomic benchmarks: a collection of datasets for genomic sequence classification

Reference 10

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Observation ff43fa94-d3f8-41d5-aa1f-be1251af5a7c · outbound

This paper cites DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing

Reference 11

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Observation 78f34821-bb7a-4353-921f-4b4bba8d85e7 · outbound

This paper cites Long short-term memory.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Long short-term memory

Reference 12

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Observation c7c03a4e-774f-4983-8289-73550d1b6a67 · outbound

This paper cites Dnabert: pre-trained bidirectional encoder representations from transformers model for dna-language in genome.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Dnabert: pre-trained bidirectional encoder representations from transformers model for dna-language in genome

Reference 13

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation c8a73d20-08a4-4e51-8628-dc3dafaecc39 · outbound

This paper cites Highly accurate protein structure prediction with alphafold.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Highly accurate protein structure prediction with alphafold

Reference 14

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Observation 11bc8219-3d23-499e-9427-b08644238ab8 · outbound

This paper cites Deep learning.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Deep learning

Reference 15

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Observation 659d61a6-4075-42f4-af41-047cfedb038b · outbound

This paper cites Hyenadna: Long-range genomic sequence modeling at single nucleotide resolution.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Hyenadna: Long-range genomic sequence modeling at single nucleotide resolution

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 2de42923-93f3-4d41-9fe7-0e056588e7a3 · outbound

This paper cites Hyena hierarchy: Towards larger convolutional language models.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Hyena hierarchy: Towards larger convolutional language models

Reference 17

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 0c3d39a9-594c-4e21-9248-c44c5d025c34 · outbound

This paper cites Dna language model grover learns sequence context in the human genome.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Dna language model grover learns sequence context in the human genome

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 56e381a1-94b0-474b-9291-c69cbda59dbb · outbound

This paper cites Unlocking gene regulation with sequence-to-function models.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Unlocking gene regulation with sequence-to-function models

Reference 19

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation ac98cee6-b1b7-4513-916d-1a2674ee0aff · outbound

This paper cites Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling

Reference 20

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Observation 38717af3-bab3-4d49-8c34-693f6034d88b · outbound

This paper cites Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

Reference 21

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Observation 1b0fc6c4-36c4-4b13-9102-e6a29e814b30 · outbound

This paper cites DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome.

NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome

Reference 22

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Pith citing papers

No inbound Pith citation observations are available.