Paper Citation Record · LEDGER

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences

As of 9 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2506.10271.

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

pith.paper-citation-record.v1
2506.10271 v3

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:36:22.182969Z

measured 36 of 36 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

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0d280475-d258-453c-8541-f229eafb6a68 · outbound

This paper cites Ge- nomic language models: opportunities and challenges.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Ge- nomic language models: opportunities and challenges

Reference 1

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Observation a8e9e8bc-777b-4b28-a520-1bae45efd1ef · outbound

This paper cites Transformers and genome language models.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Transformers and genome language models

Reference 2

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Observation b4274879-6b91-4907-adf4-e21d6c9499a9 · outbound

This paper cites Efficient evolution of human antibodies from general protein language models.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Efficient evolution of human antibodies from general protein language models

Reference 3

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This paper cites Protein language models-assisted optimiza- tion of a uracil-n-glycosylase variant enables programmable t-to-g and t-to-c base editing.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Protein language models-assisted optimiza- tion of a uracil-n-glycosylase variant enables programmable t-to-g and t-to-c base editing

Reference 4

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Observation db67a4cf-d8ea-48e5-bec0-a7fac20c99ed · outbound

This paper cites Integrating protein language models and automatic biofoundry for enhanced protein evolution.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Integrating protein language models and automatic biofoundry for enhanced protein evolution

Reference 5

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Observation e08b8cbd-a4b2-4370-9e48-50c8c43576d8 · outbound

This paper cites Saprothub: Making protein modeling accessible to all biologists.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Saprothub: Making protein modeling accessible to all biologists

Reference 6

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Observation d828c90e-6586-4986-a248-7347e27be054 · outbound

This paper cites Proteingym: Large-scale benchmarks for protein fitness prediction and design.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Proteingym: Large-scale benchmarks for protein fitness prediction and design

Reference 7

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Observation e646981d-ae97-456f-a6ae-3745b2090800 · outbound

This paper cites Dna language models are powerful predictors of genome-wide variant effects.Proceedings of the National Academy of Sciences, 120(44):e2311219120, 2023.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Dna language models are powerful predictors of genome-wide variant effects.Proceedings of the National Academy of Sciences, 120(44):e2311219120, 2023

Reference 8

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Observation 90cdb147-15d8-4e6b-9577-157fb3ee90a0 · outbound

This paper cites A 5’ utr language model for decoding untranslated regions of mrna and function predictions.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences A 5’ utr language model for decoding untranslated regions of mrna and function predictions

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:36:22.498606Z

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Observation 03d09c6e-5bae-40f1-b3f1-c66f13457ea9 · outbound

This paper cites Evaluating the representational power of pre-trained dna language models for regulatory genomics.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Evaluating the representational power of pre-trained dna language models for regulatory genomics

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:36:22.489687Z

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Observation 25726d22-01b3-477f-8850-3cf86175a76c · outbound

This paper cites Synthetic design of strong promoters.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Synthetic design of strong promoters

Reference 11

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Observation e3590563-44c2-4efd-997a-b0baf528a9e8 · outbound

This paper cites miRNA circuit modules for precise, tunable control of gene expression.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences miRNA circuit modules for precise, tunable control of gene expression

Reference 12

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Observation b7aebebc-cf3c-433e-905b-c417e5a69c8b · outbound

This paper cites Applications of synthetic biology in medical and pharmaceutical fields.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Applications of synthetic biology in medical and pharmaceutical fields

Reference 13

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Observation 0eb69525-1888-483b-b151-973808e20d5c · outbound

This paper cites Predicting bacterial promoter function and evolution from random sequences.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Predicting bacterial promoter function and evolution from random sequences

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:36:22.455387Z

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Observation 96312500-0c95-40d7-a8b5-0ba684912696 · outbound

This paper cites Deciphering eukaryotic gene-regulatory logic with 100 million ran- dom promoters.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Deciphering eukaryotic gene-regulatory logic with 100 million ran- dom promoters

Reference 15

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Observation a36324b8-089a-465b-8388-08aa24bd866e · outbound

This paper cites Composability of regulatory sequences controlling transcription and translation in escherichia coli.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Composability of regulatory sequences controlling transcription and translation in escherichia coli

Reference 16

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Observation ba4fa423-2cf8-4c19-91f1-f42ad212561f · outbound

This paper cites A massively parallel reporter assay library to screen short synthetic promoters in mammalian cells.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences A massively parallel reporter assay library to screen short synthetic promoters in mammalian cells

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:36:22.428237Z

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Observation 3ebb9fbd-8300-49ca-ac28-e521bc12799c · outbound

This paper cites Generanno: A genomic foundation model for metagenomic annotation.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Generanno: A genomic foundation model for metagenomic annotation

Reference 18

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Observation 33da87f4-4280-4c5f-8c64-be77f26807fa · outbound

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

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 19

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Observation 844157dc-dd6c-4758-aab4-b2df94e157ff · outbound

This paper cites Lan- guage models are few-shot learners.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Lan- guage models are few-shot learners

Reference 20

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Observation cdcd406c-e366-470f-8d3e-6f5d10aebb30 · outbound

This paper cites StripedHyena: Moving Beyond Transformers with Hybrid Signal Pro- cessing Models, 12 2023.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences StripedHyena: Moving Beyond Transformers with Hybrid Signal Pro- cessing Models, 12 2023

Reference 21

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Observation 0a45b6e6-2076-4106-a871-08beef6aac9b · outbound

This paper cites Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale

Reference 22

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This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 23

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This paper cites BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model

Reference 24

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This paper cites Protein language model fitness is a matter of preference.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Protein language model fitness is a matter of preference

Reference 25

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Observation 8304801a-a44c-411f-9eec-ee6113280e32 · outbound

This paper cites METAGENE-1: Metagenomic Foundation Model for Pandemic Monitoring.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences METAGENE-1: Metagenomic Foundation Model for Pandemic Monitoring

Reference 26

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

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Nucleotide transformer: building and evaluating robust foundation models for human genomics

Reference 27

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Observation 50610b60-3e4d-41c8-83d1-34c3c92eb1a5 · outbound

This paper cites Generator: A long-context generative genomic foundation model.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Generator: A long-context generative genomic foundation model

Reference 28

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Observation 769c0cd5-969f-451c-a550-03c0d0d0f090 · outbound

This paper cites Sequence modeling and design from molecular to genome scale with evo.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Sequence modeling and design from molecular to genome scale with evo

Reference 29

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Observation a89d0f6b-f485-41a1-afbf-ccb1eab0ea44 · outbound

This paper cites Semantic mining of functional de novo genes from a genomic language model.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Semantic mining of functional de novo genes from a genomic language model

Reference 30

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This paper cites Genome modeling and design across all domains of life with evo 2.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Genome modeling and design across all domains of life with evo 2

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:36:22.353882Z

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Observation 7b0a3283-ed0b-4b25-a95b-460161addb09 · outbound

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

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome

Reference 32

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Observation 8960a444-7d80-4bf1-b074-fc8f5f828904 · outbound

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

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T04:36:21.802097Z

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Observation ca39ebd7-f1e6-4807-85cc-a1f5cbe6f3bb · outbound

This paper cites Benchmarking dna sequence models for causal regulatory variant prediction in human genetics.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences Benchmarking dna sequence models for causal regulatory variant prediction in human genetics

Reference 34

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Observation adf8f8d7-e30d-4992-b186-3bd72abca757 · outbound

This paper cites The omg dataset: An open metagenomic corpus for mixed-modality genomic language modeling.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences The omg dataset: An open metagenomic corpus for mixed-modality genomic language modeling

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:36:22.336653Z

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Observation 213b407d-774e-44d5-a6e5-a6c44d036835 · outbound

This paper cites nucleotide- transformer-2.5b-multi-species.

Evaluating DNA function understanding in genomic language models using evolutionarily implausible sequences nucleotide- transformer-2.5b-multi-species

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:36:22.327156Z

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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