Paper Citation Record · LEDGER

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods

As of 13 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2411.17669.

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

pith.paper-citation-record.v1
2411.17669 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:57:41.076689Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

30 of 30 outbound references displayed

verified exact1
verified fuzzy24
unresolved5
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 76ff52db-4d43-400e-9e0f-f39a58967edc · outbound

This paper cites Transforming the language of life: transformer neural net works for protein prediction tasks,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Transforming the language of life: transformer neural net works for protein prediction tasks,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.414210Z

Source-reported events for the cited work

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Observation ea6049a1-f1cd-4771-a621-2544317de0d4 · outbound

This paper cites Biological structure and function emerge from scaling unsupervised learning to 250 million protein s equences,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Biological structure and function emerge from scaling unsupervised learning to 250 million protein s equences,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.403792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation de6e54a1-2312-4974-a851-6eb7d8f86011 · outbound

This paper cites Prottrans: Toward understanding the language of life through self-supervise d learning,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Prottrans: Toward understanding the language of life through self-supervise d learning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.391493Z

Source-reported events for the cited work

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Observation 8389e490-bddb-44c2-9998-8f2ea675177b · outbound

This paper cites The language of prote ins: Nlp, machine learning & protein sequences,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods The language of prote ins: Nlp, machine learning & protein sequences,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.381899Z

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Observation db9c60d2-a273-4e60-a921-67a16b3d8d11 · outbound

This paper cites Proteinbert: a universal deep-learning model of protein sequence and fun ction,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Proteinbert: a universal deep-learning model of protein sequence and fun ction,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.370982Z

Source-reported events for the cited work

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Observation 6455a2c0-e70e-4094-81ae-71d4f8ad64d7 · outbound

This paper cites Evolutionary-scale prediction of atomic- level protein structure with a language model,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Evolutionary-scale prediction of atomic- level protein structure with a language model,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.361134Z

Source-reported events for the cited work

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Observation 0fe3da6f-ef84-45e2-bf50-df1cb35790f5 · outbound

This paper cites Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling

Reference 7

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Source-reported events for the cited work

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Observation 35c7243d-f71c-4328-9eec-67290902e2ae · outbound

This paper cites PETA: Evaluating the Impact of Protein Transfer Learning with Sub-word Tokenization on Downstream Applications.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods PETA: Evaluating the Impact of Protein Transfer Learning with Sub-word Tokenization on Downstream Applications

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-12T11:57:41.129445Z

Source-reported events for the cited work

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Observation b799efa8-bcf0-4e9d-a622-c2e84545f277 · outbound

This paper cites Effect o f tokenization on transformers for biological sequences,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Effect o f tokenization on transformers for biological sequences,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.350937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e7977724-d930-4994-a4b6-d5ed98409ef4 · outbound

This paper cites Protein langu age models meet reduced amino acid alphabets,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Protein langu age models meet reduced amino acid alphabets,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.340696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 85d7566b-4d83-4bbf-9976-83707e44f944 · outbound

This paper cites BERTology Meets Biology: Interpreting Attention in Protein Language Models.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods BERTology Meets Biology: Interpreting Attention in Protein Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T11:57:41.013343Z

Source-reported events for the cited work

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Observation 4dd0b20f-b526-495c-873f-94a2e4bdfb59 · outbound

This paper cites Transformer protein language models are unsupervised structure learne rs,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Transformer protein language models are unsupervised structure learne rs,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.330074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ccb9840c-07c9-431f-82ed-91d9eacafbab · outbound

This paper cites Exploring data-driven chem ical smiles tokenization approaches to identify key protein–ligand bi nding moieties,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Exploring data-driven chem ical smiles tokenization approaches to identify key protein–ligand bi nding moieties,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.319052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0914d502-03b6-47f3-8275-7ad58ac4cc5c · outbound

This paper cites The organization of domains in proteins obeys menzerath-altmann’s law of lan guage,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods The organization of domains in proteins obeys menzerath-altmann’s law of lan guage,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.308970Z

Source-reported events for the cited work

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Observation 45257caf-bcca-4b05-85df-6947a33aeed0 · outbound

This paper cites Ling uistic laws in biology,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Ling uistic laws in biology,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.299240Z

Source-reported events for the cited work

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Observation d86ec17f-0f09-4a1c-be31-d27329d7977f · outbound

This paper cites Sapr ot: Protein language modeling with structure-aware vocabulary,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Sapr ot: Protein language modeling with structure-aware vocabulary,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.289176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3b43bea3-5741-4482-a8a0-736bf77d234b · outbound

This paper cites Bilingual language model for pr otein sequence and structure,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Bilingual language model for pr otein sequence and structure,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.279029Z

Source-reported events for the cited work

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Observation 990981be-718c-488b-99e1-db602752f0b3 · outbound

This paper cites Fast and accurate protein structure search with foldseek,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Fast and accurate protein structure search with foldseek,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.269181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e9eafabb-18ee-4383-a2d5-9a8f545daad9 · outbound

This paper cites Neural machine tr anslation of rare words with subword units,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Neural machine tr anslation of rare words with subword units,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.259454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7b3f1669-51f6-4054-8424-b6b2c5fd8c81 · outbound

This paper cites Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T11:57:41.042802Z

Source-reported events for the cited work

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Observation 6dfb1f17-ffec-4bbe-b604-c682942cb75b · outbound

This paper cites Sentencepiece: A simple and language inde- pendent subword tokenizer and detokenizer for neural text p rocessing,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Sentencepiece: A simple and language inde- pendent subword tokenizer and detokenizer for neural text p rocessing,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.249087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f1419a2c-ecf4-433b-9e76-be8d05484eef · outbound

This paper cites Subword regularization: Improving neural ne twork transla- tion models with multiple subword candidates,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Subword regularization: Improving neural ne twork transla- tion models with multiple subword candidates,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.237662Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ce91a240-4115-4a1b-845f-37bedf444533 · outbound

This paper cites Uniref: comprehensive and non-redundant uniprot referen ce clusters,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Uniref: comprehensive and non-redundant uniprot referen ce clusters,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.226498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6a45fb72-dc20-4921-b016-9734300e88a7 · outbound

This paper cites Pointe r sentinel mixture models,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Pointe r sentinel mixture models,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.215697Z

Source-reported events for the cited work

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Observation 1538b3ee-5d28-4265-af2f-d6fd3102e88a · outbound

This paper cites Incorporating context into subword vocabu- laries,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Incorporating context into subword vocabu- laries,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.204674Z

Source-reported events for the cited work

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Observation 59f4f506-49ca-4391-931c-e2ae6116cbc2 · outbound

This paper cites New and continuing develo pments at prosite,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods New and continuing develo pments at prosite,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.193591Z

Source-reported events for the cited work

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Observation b15398e9-4ed9-4f74-b9d9-076da6246af1 · outbound

This paper cites an unresolved cited work.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:57:41.183329Z

Source-reported events for the cited work

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Observation db8860f0-9487-455b-91e4-9eb26da019df · outbound

This paper cites On the physical origin of linguistic laws and lo gnormality in speech,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods On the physical origin of linguistic laws and lo gnormality in speech,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.172103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 236c4f65-26d4-4746-8d2c-d205aff38675 · outbound

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Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:57:41.160960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 250c5b75-d9c9-4177-86d1-1cd61dd288f7 · outbound

This paper cites Prolegomena to menzerath’s law,.

Linguistic Laws Meet Protein Sequences: A Comparative Analysis of Subword Tokenization Methods Prolegomena to menzerath’s law,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:57:41.150180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.