Paper Citation Record · LEDGER

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA

As of 21 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 3 inbound Pith citation observations for arXiv:2412.13716.

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

pith.paper-citation-record.v1
2412.13716 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

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measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:16:49.178190Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T10:23:02.033144Z

Reference resolution

62 of 62 outbound references displayed

verified exact1
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unresolved45
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External citation measurements

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Outbound references

Observation adf20a5d-0e1e-4f6a-b885-ba74e95b579d · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 1

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Observation 96ccb9b7-169a-4329-a7a2-becced86e07b · outbound

This paper cites Language models are few-shot learners.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Language models are few-shot learners

Reference 2

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Observation c1023c1d-a123-4f3b-bb74-137c2ceae6bb · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA LLaMA: Open and Efficient Foundation Language Models

Reference 3

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Observation 0343c83b-b6d0-4b3d-bc8d-be602420b5fa · outbound

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

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Dnabert: pre-trained bidirectional encoder representations from transformers model for dna-language in genome

Reference 4

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Observation a053cc48-3d4f-4380-bb47-2574e3b41884 · outbound

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

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome

Reference 5

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Observation 2f04952a-743f-47fc-a4f3-34d724ef55a2 · outbound

This paper cites de Almeida, Hassan Sirelkha- tim, Guillaume Richard, Marcin Skwark, Karim Beguir, Marie Lopez, and Thomas Pierrot.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA de Almeida, Hassan Sirelkha- tim, Guillaume Richard, Marcin Skwark, Karim Beguir, Marie Lopez, and Thomas Pierrot

Reference 6

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Observation c0c5fb7b-d119-4441-af72-091f61a2c035 · outbound

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

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Hyenadna: Long-range genomic sequence modeling at single nucleotide resolution

Reference 7

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Observation 227e9583-5913-4b88-8371-903bf7d71271 · outbound

This paper cites Linguistically inspired roadmap for building biologically reliable protein language models.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Linguistically inspired roadmap for building biologically reliable protein language models

Reference 8

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Observation 98b275a4-823e-4690-beb0-49a3e7cdd0dd · outbound

This paper cites Toward Understanding BERT-Like Pre-Training for DNA Foundation Models.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Toward Understanding BERT-Like Pre-Training for DNA Foundation Models

Reference 9

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Observation 20b01f65-b8a3-4628-bffd-85c2448147b8 · outbound

This paper cites Byte Pair Encoding is Suboptimal for Language Model Pretraining.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Byte Pair Encoding is Suboptimal for Language Model Pretraining

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.380621Z

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Observation 63408308-2bc7-4487-a93c-60b8ed154614 · outbound

This paper cites Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words

Reference 11

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Observation d2ca6f03-68c3-4879-8781-7837c5c3c6f5 · outbound

This paper cites Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:55:41.076099Z

Source-reported events for the cited work

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Observation 71f7cc33-6739-461b-8d5f-f62a93a255ca · outbound

This paper cites A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:55:41.066015Z

Source-reported events for the cited work

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Observation 36ee5187-cba8-48f4-bcda-1a611208da56 · outbound

This paper cites Central dogma of molecular biology.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Central dogma of molecular biology

Reference 14

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Observation 2f0f97b8-d4a5-4471-b934-5d16c6230897 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 15

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Observation 0be5b152-b60c-4ffe-a73b-2efec330b66b · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 16

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Observation 614806e1-d8ae-459c-a26c-ca2c343a7208 · outbound

This paper cites Mixtral of Experts.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Mixtral of Experts

Reference 17

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Source-reported events for the cited work

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Observation 59bf2de7-2fa0-445f-a393-bf9d95f32e84 · outbound

This paper cites Deformable convolutional networks.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Deformable convolutional networks

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:55:41.044498Z

Source-reported events for the cited work

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Observation 3fd807a2-0241-41a0-9e43-bda2870f2af3 · outbound

This paper cites Deformable convnets v2: More deformable, better results.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Deformable convnets v2: More deformable, better results

Reference 19

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Observation 7cd9417a-3c93-47a4-9d97-1cdc3e36638d · outbound

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

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Genomic benchmarks: a collection of datasets for genomic sequence classification

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:55:41.024150Z

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Observation 3e10a6ba-ab17-4c77-81e1-f55599267160 · outbound

This paper cites Character-aware neural language models.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Character-aware neural language models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:55:41.014164Z

Source-reported events for the cited work

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Observation 0663aba8-d91f-414f-9846-b3f1d3ec83d0 · outbound

This paper cites Character-level language modeling with deeper self-attention.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Character-level language modeling with deeper self-attention

Reference 22

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Observation 49f8210d-6aac-49de-a5ca-af7749ae835c · outbound

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

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling

Reference 23

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Observation 033151c5-1dd7-4ce3-a2db-d91108a6f9e8 · outbound

This paper cites Class-based n-gram models of natural language.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Class-based n-gram models of natural language

Reference 24

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Observation 1725b71f-4503-475e-847f-fb45b583d6c2 · outbound

This paper cites Character n-gram tokenization for european language text retrieval.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Character n-gram tokenization for european language text retrieval

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:55:40.991377Z

Source-reported events for the cited work

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Observation de6cf4ef-d973-4008-bfc2-1e2d04b3c3fc · outbound

This paper cites Dnagpt: A generalized pretrained tool for multiple dna sequence analysis tasks.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Dnagpt: A generalized pretrained tool for multiple dna sequence analysis tasks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:55:40.980111Z

Source-reported events for the cited work

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Observation 5caeed51-0007-4ee3-8c0b-f20ee2a148db · outbound

This paper cites Neural Machine Translation of Rare Words with Subword Units.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Neural Machine Translation of Rare Words with Subword Units

Reference 27

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Source-reported events for the cited work

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Observation 54dd53c3-596c-4607-a572-cb7f98321f25 · outbound

This paper cites High-coverage whole-genome sequencing of the expanded 1000 genomes project cohort including 602 trios.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA High-coverage whole-genome sequencing of the expanded 1000 genomes project cohort including 602 trios

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:55:40.968978Z

Source-reported events for the cited work

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Observation 548b1e24-3082-4dd6-9b57-bea456d7c720 · outbound

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

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.447528Z

Source-reported events for the cited work

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Observation 38540933-d599-49ab-a736-31daa58ac41f · outbound

This paper cites Neural discrete representation learning.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Neural discrete representation learning

Reference 30

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Source-reported events for the cited work

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Observation 17c9714b-a969-45b1-98ea-be6f86316724 · outbound

This paper cites VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.455960Z

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Observation 2f7b6a10-c70c-44e0-ae2b-7f594e9e7ca3 · outbound

This paper cites Evaluation of grch38 and de novo haploid genome assemblies demonstrates the enduring quality of the reference assembly.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Evaluation of grch38 and de novo haploid genome assemblies demonstrates the enduring quality of the reference assembly

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.460328Z

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Observation 7750e028-29f0-48fb-9929-64c90753e852 · outbound

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

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Hyena hierarchy: Towards larger convolutional language models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.464808Z

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Observation 6ee214f2-8e14-4205-8d03-e96064ea4e51 · outbound

This paper cites Generative pretraining from pixels.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Generative pretraining from pixels

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.468527Z

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Observation eebd56d4-7f5b-4461-95d3-602fc33e4280 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.471958Z

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Observation c1f83ec1-12a6-4d32-aef1-40d4cbe5cf71 · outbound

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Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Zero-shot text-to-image generation

Reference 36

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Source-reported events for the cited work

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Observation f4ce9722-1672-46cf-82d2-fa03f107322f · outbound

This paper cites BEiT: BERT Pre-Training of Image Transformers.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA BEiT: BERT Pre-Training of Image Transformers

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.479394Z

Source-reported events for the cited work

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Observation c411ea1e-98a0-40df-9cb7-be68020efbe0 · outbound

This paper cites Bottom-up and top-down attention for image captioning and visual question answering.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Bottom-up and top-down attention for image captioning and visual question answering

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.483300Z

Source-reported events for the cited work

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Observation 398af155-0664-4a74-bfaf-e41756590a1f · outbound

This paper cites Multimodal pretraining unmasked: A meta-analysis and a unified framework of vision-and-language berts.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Multimodal pretraining unmasked: A meta-analysis and a unified framework of vision-and-language berts

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:55:40.923067Z

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Observation 223283a5-be74-4f15-a3b9-acf5aad8ee96 · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Faster r-cnn: Towards real-time object detection with region proposal networks

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.490900Z

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Observation 10e95a37-320b-4efe-af4c-28803fea135b · outbound

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Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Subobject-level Image Tokenization

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.494350Z

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Observation b3fb685d-9bdb-44b7-8288-55b186f82c8f · outbound

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Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Vision Transformers with Natural Language Semantics

Reference 42

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28f38dbc-8ca1-434d-b37c-d74f6d035824 · outbound

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Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Segment anything

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.503210Z

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Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Scaling physics-informed hard constraints with mixture-of-experts

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.507185Z

Source-reported events for the cited work

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Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Conformer: Convolution-augmented Transformer for Speech Recognition

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.510711Z

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Observation 10284f4a-1580-4a51-a7d4-bb92a635595d · outbound

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Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Cvt: Introducing convolutions to vision transformers

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.514306Z

Source-reported events for the cited work

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Observation 6526c71e-6e77-4a94-9f98-06b6a6f9eca4 · outbound

This paper cites Early convolutions help transformers see better.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Early convolutions help transformers see better

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:55:40.895999Z

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Observation 61363b4a-b239-42ee-9a90-004c907fff9c · outbound

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

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Effective gene expression prediction from sequence by integrating long-range interactions

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.520711Z

Source-reported events for the cited work

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Observation 112ee4f8-b003-45db-9c15-855991b4a09c · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Roformer: Enhanced transformer with rotary position embedding

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.524862Z

Source-reported events for the cited work

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Observation 0ca6c11d-4ab1-46c3-85ca-9b11ae2bd8a4 · outbound

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Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA GLU Variants Improve Transformer

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.528480Z

Source-reported events for the cited work

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Observation b0c40b5d-6265-486e-a649-53051191d6db · outbound

This paper cites Palm: Scaling language modeling with pathways.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Palm: Scaling language modeling with pathways

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.533028Z

Source-reported events for the cited work

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Observation 962c133b-2c4d-4c85-8368-35fcb6ff3d42 · outbound

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Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Flashattention: Fast and memory-efficient exact attention with io-awareness

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.536354Z

Source-reported events for the cited work

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Observation 86ba2652-d4fe-469f-87f2-280e2a026a0b · outbound

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

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.540437Z

Source-reported events for the cited work

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Observation dcf7c438-aa73-448e-a718-ca7a873fc761 · outbound

This paper cites PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:55:40.862765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b0e6f61c-6360-4a71-9d15-aac413da8ee4 · outbound

This paper cites PyTorch Lightning, March 2019.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA PyTorch Lightning, March 2019

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:55:40.853331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cad24a14-2895-4245-91d5-4ea2287dd92e · outbound

This paper cites an unresolved cited work.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Unresolved cited work

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.551231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b20f5e8-61b2-419a-8486-4f2b454cfdef · outbound

This paper cites pybind11 – seamless operability between c++11 and python, 2017.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA pybind11 – seamless operability between c++11 and python, 2017

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.554545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be6885a0-f91a-42ce-a694-804c7596a59d · outbound

This paper cites Pedregosa, G.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Pedregosa, G

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.558478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0ab5825-0e40-4ab8-886b-0bf3d0249f86 · outbound

This paper cites Harris, K.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Harris, K

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T12:55:40.563119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35b57e7f-6ac5-4714-99e2-bc0174190c76 · outbound

This paper cites an unresolved cited work.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-11T12:55:40.821524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 067297f9-871f-44e9-a133-37c46ab24599 · outbound

This paper cites 1-mer” stands for single nucleotide. “Ovlp 6-mer.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA 1-mer” stands for single nucleotide. “Ovlp 6-mer

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:55:40.811549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2eb340a1-b443-4b4d-86e2-cc2b084d7f47 · outbound

This paper cites an unresolved cited work.

Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA Unresolved cited work

Reference 510

Resolution: unresolved; raw_fallback, observed 2026-08-11T12:55:40.801617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 54c58040-082d-4fc1-bfc6-72ae83cfd73b · inbound

Omni-DNA: A Unified Genomic Foundation Model for Cross-Modal and Multi-Task Learning cites this paper.

Omni-DNA: A Unified Genomic Foundation Model for Cross-Modal and Multi-Task Learning Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-09T10:16:49.178190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68266149-a31b-4fca-8003-04989623e289 · inbound

Multimodal Medical Code Tokenizer cites this paper.

Multimodal Medical Code Tokenizer Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-09T00:42:57.108037Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebe9fc7f-fa4f-4d2b-a32e-00191f17e3d9 · inbound

Agentomics-ML: Autonomous Machine Learning Experimentation Agent for Genomic and Transcriptomic Data cites this paper.

Agentomics-ML: Autonomous Machine Learning Experimentation Agent for Genomic and Transcriptomic Data Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:23:02.080819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.