Paper Citation Record · LEDGER

Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2405.17067.

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

pith.paper-citation-record.v1
2405.17067 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:06:01.347468Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T13:22:19.233640Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d1c171f5-3432-4b96-bc8c-508bb20fb113 · inbound

CBEval: A framework for evaluating and interpreting cognitive biases in LLMs cites this paper.

CBEval: A framework for evaluating and interpreting cognitive biases in LLMs Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T22:54:51.467207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f488651-eff9-4091-95f4-1c267f0e708b · inbound

YuLan-Mini: An Open Data-efficient Language Model cites this paper.

YuLan-Mini: An Open Data-efficient Language Model Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization

Reference 103

Resolution: unresolved; no resolver link, observed 2026-08-11T05:17:55.901843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd1a9440-7754-4e71-b165-84e7f30fa935 · inbound

Multimodal Medical Code Tokenizer cites this paper.

Multimodal Medical Code Tokenizer Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-09T00:42:57.175237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e98f4c26-e3b2-4d26-8e56-9a215d2b698a · inbound

Enhancing Text-to-Image Diffusion Transformer via Split-Text Conditioning cites this paper.

Enhancing Text-to-Image Diffusion Transformer via Split-Text Conditioning Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-19T13:22:19.235519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2181d440-b5cf-4ce0-8452-4e3c5006006c · inbound

Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese cites this paper.

Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T13:08:36.997337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf7f876f-ef55-4b09-a7c7-52439b491d0e · inbound

Causal Estimation of Tokenisation Bias cites this paper.

Causal Estimation of Tokenisation Bias Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T11:15:44.719192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45ff1f85-be70-4091-8d5e-bfe25bdfdada · inbound

Incorporating Domain Knowledge into Materials Tokenization cites this paper.

Incorporating Domain Knowledge into Materials Tokenization Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:26.697578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9e906c5-9e18-4f44-91dd-90d8e15033e8 · inbound

Concept-Level AI for Telecom: Moving Beyond Large Language Models cites this paper.

Concept-Level AI for Telecom: Moving Beyond Large Language Models Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T22:08:32.962081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2677350-24e3-4787-ae30-5e5ea32b242f · inbound

TASE: Token Awareness and Structured Evaluation for Multilingual Language Models cites this paper.

TASE: Token Awareness and Structured Evaluation for Multilingual Language Models Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-05T23:21:25.332133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d9889e1-6576-47ae-8a43-cd37d928f05f · inbound

Understanding Subword Compositionality of Large Language Models cites this paper.

Understanding Subword Compositionality of Large Language Models Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T17:06:01.347468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4652b3be-406b-4b73-b576-b13112a0d482 · inbound

Addressing Tokenization Inconsistency in Steganography and Watermarking Based on Large Language Models cites this paper.

Addressing Tokenization Inconsistency in Steganography and Watermarking Based on Large Language Models Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-05T14:57:24.794551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.