Paper Citation Record · LEDGER

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models

As of 17 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2506.15138.

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

pith.paper-citation-record.v1
2506.15138 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:47:59.758998Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

50 of 50 outbound references displayed

verified exact3
verified fuzzy1
unresolved46
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 85669550-fee2-48d9-8eb3-2cd6c9637e2c · outbound

This paper cites GPT-4 Technical Report.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.499219Z

Source-reported events for the cited work

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Observation 2789bf8d-326b-439d-90f7-2be9a4f7d70f · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:48:00.787349Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bae2c230-d894-4273-8add-201038cd7414 · outbound

This paper cites Qwen Technical Report.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Qwen Technical Report

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.510842Z

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Observation 9e77dd50-a44c-48a7-b785-bd61375af239 · outbound

This paper cites Adaptive BPE Tokenization for Enhanced Vocabulary Adaptation in Finetuning Pretrained Language Models.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Adaptive BPE Tokenization for Enhanced Vocabulary Adaptation in Finetuning Pretrained Language Models

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:48:00.424811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cffd4828-c8e9-46fe-b940-8aa8ad98e616 · outbound

This paper cites An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT).

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT)

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.521940Z

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Observation 733101f7-8814-4f71-8912-486b9cbbf11c · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.528528Z

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Observation db4457e3-6252-4afe-9da5-762f96a9ae02 · outbound

This paper cites Getting the most out of your tokenizer for pre-training and domain adaptation.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Getting the most out of your tokenizer for pre-training and domain adaptation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.534351Z

Source-reported events for the cited work

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Observation 341c825e-1042-437d-a958-29bc7bbea7c7 · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.539506Z

Source-reported events for the cited work

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Observation 0a21da18-f050-4a52-be61-65550e9a292c · outbound

This paper cites How do different tokenizers perform on downstream tasks in scriptio continua languages?: A case study in Japanese.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models How do different tokenizers perform on downstream tasks in scriptio continua languages?: A case study in Japanese

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:48:00.353839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8f92c986-c3c4-4fba-9b24-99655f8dd1ba · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.549720Z

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Observation 9e8ff8d0-00ac-4026-a0e4-52813f9448d7 · outbound

This paper cites Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performance.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unpacking Tokenization: Evaluating Text Compression and its Correlation with Model Performance

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.554432Z

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Observation 0f6d8bf5-a482-459c-8c63-882cf13e938a · outbound

This paper cites The Llama 3 Herd of Models.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models The Llama 3 Herd of Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.559384Z

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Observation c4cec494-d386-4138-b3c1-2a5264af483b · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.564377Z

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Observation f78465ae-67c3-4c52-ab8c-63d03f45f7c2 · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:48:00.744252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c3cf55d2-6958-439d-b7c5-068a4e2dd097 · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.574424Z

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Observation 73793c27-5a6b-456f-a8ca-52424b03b142 · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:48:00.714519Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b62926b5-3e24-4dae-9b53-455cb89f8c62 · outbound

This paper cites What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.585032Z

Source-reported events for the cited work

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Observation b80bdba0-f3f6-4d44-b8a4-2ddea4b65272 · outbound

This paper cites Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.590138Z

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Observation 13b715a8-d174-4b1e-b7c2-defd639c9e5a · outbound

This paper cites SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.595322Z

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Observation 4f2780a0-6ce0-46ae-96f3-b5a5fc9d8b95 · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:48:00.698482Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 270361d5-5b98-4203-8fac-23144bed1b1a · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:48:00.682379Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2dc0644f-1ccb-4f32-8429-fc5979070045 · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.610890Z

Source-reported events for the cited work

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Observation 98c41c12-dbd7-420c-9dae-edc9b3992592 · outbound

This paper cites DeepSeek-V3 Technical Report.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models DeepSeek-V3 Technical Report

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.617025Z

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Observation 62e473fe-81fb-4462-af56-a8d1079d6426 · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.622788Z

Source-reported events for the cited work

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Observation 7b87be74-0718-49c4-857b-bfd937bc2e48 · outbound

This paper cites Decoupled Weight Decay Regularization.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Decoupled Weight Decay Regularization

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.628541Z

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Observation fcd4ba1f-f441-4d7b-8a81-415fab548fc0 · outbound

This paper cites Meta Platforms.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Meta Platforms

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:48:00.656084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 30f439af-c322-42e0-8f82-ad13eecbb48f · outbound

This paper cites Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.638067Z

Source-reported events for the cited work

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Observation 66be9740-471d-4b4d-8591-69fb407bb3b4 · outbound

This paper cites An Empirical Study of Tokenization Strategies for Various Korean NLP Tasks.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models An Empirical Study of Tokenization Strategies for Various Korean NLP Tasks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.643267Z

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Observation 3c7ce041-7597-47e7-9c25-1d27e7db5d9d · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.649040Z

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Observation 12210d51-8fdc-475c-9b85-d63debcaca45 · outbound

This paper cites BPE-Dropout: Simple and Effective Subword Regularization.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models BPE-Dropout: Simple and Effective Subword Regularization

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.656812Z

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Observation 9e3d8d42-3a1e-4430-a935-f97cf2bc891e · outbound

This paper cites Zero Bubble Pipeline Parallelism.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Zero Bubble Pipeline Parallelism

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.661858Z

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Observation b095c7b8-2a11-43a3-8012-bdd2f98125ce · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.667109Z

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Observation ede2af3e-0a26-4c52-aa60-30649f6edb19 · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.672144Z

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Observation b0920566-f3ef-480d-874d-79fc57e6e13e · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.677496Z

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Observation c48cd711-45f0-4228-95ad-5fb375a59087 · outbound

This paper cites How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.682281Z

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Observation a844923e-b48f-4ee3-af77-939855f6b295 · outbound

This paper cites Tokenization Is More Than Compression.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Tokenization Is More Than Compression

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.687743Z

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Observation 663b83ba-8f9e-448f-974b-c2b492946fe7 · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.692744Z

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Observation 5b6daecf-4ce1-44eb-874a-b68605080614 · outbound

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

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Neural Machine Translation of Rare Words with Subword Units

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.697464Z

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Observation 28a55d12-84f1-42a4-a04f-6251fa34c5d1 · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.702982Z

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Unavailable: canonical work link unavailable.

Observation a06282b0-b9af-4278-8eae-5bbe90737881 · outbound

This paper cites GraphBPE: Molecular Graphs Meet Byte-Pair Encoding.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models GraphBPE: Molecular Graphs Meet Byte-Pair Encoding

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.708390Z

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Observation ede9fb37-7323-40da-ad93-437a2ae4f719 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Gemma 2: Improving Open Language Models at a Practical Size

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.713821Z

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Observation 2c2ff89f-1880-4dc8-9817-8bdced36407b · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:48:00.576646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b7e52288-1eb2-47fa-8dd0-165a107a50c5 · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.724409Z

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Observation c729b015-b5da-4362-854c-f21b28201505 · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 44

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:48:00.542767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ca33ab7b-9023-43de-9866-0836d7d2750a · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.734578Z

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Unavailable: canonical work link unavailable.

Observation 62662ad5-5887-4af7-b6a9-c466c1c5c735 · outbound

This paper cites Incorporating Context into Subword Vocabularies.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Incorporating Context into Subword Vocabularies

Reference 46

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:47:59.830216Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 61c4df7f-8b48-41f6-8eb7-1eee03035ef1 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.744094Z

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Observation 97e3abbc-fa98-4126-a164-b72201ff0742 · outbound

This paper cites an unresolved cited work.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:48:00.508061Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4c745d4c-f867-4088-8ba9-09f34a09e274 · outbound

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Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models URL: " 'urlintro :=

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.753544Z

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Observation a331d249-8366-463f-a9a3-1fa6767d7d33 · outbound

This paper cites write newline.

Thunder-Tok: Minimizing Tokens per Word in Tokenizing Korean Texts for Generative Language Models write newline

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Resolution: unresolved; no resolver link, observed 2026-08-15T19:47:59.758998Z

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Pith citing papers

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