Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T11:57:41.076689Z
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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
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
- metadata mismatch0
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
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
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 2026-08-13T06:32:02.005865+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
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
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
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
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
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:57:40.998637Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
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 2026-08-13T06:32:02.005865+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 2026-08-13T06:32:02.005865+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
Unavailable: canonical work link unavailable.
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 2026-08-13T06:32:02.005865+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 2026-08-13T06:32:02.005865+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
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
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
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
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 2026-08-13T06:32:02.005865+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
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
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 2026-08-13T06:32:02.005865+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 2026-08-13T06:32:02.005865+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
Unavailable: canonical work link unavailable.
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 2026-08-13T06:32:02.005865+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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+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 2026-08-13T06:32:02.005865+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
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
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
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
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
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
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
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
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 2026-08-13T06:32:02.005865+00:00.
Observation 236c4f65-26d4-4746-8d2c-d205aff38675 · outbound
This paper cites an unresolved cited work.
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 2026-08-13T06:32:02.005865+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 2026-08-13T06:32:02.005865+00:00.
Pith citing papers
No inbound Pith citation observations are available.