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Paper Citation Record · LEDGER

Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 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 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:08:36.997337Z

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 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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-19T13:19:20.215467Z digest=sha256:06f7e0cb03c136891eae69f33ac0d0acb185988c9f28d9a350957d2226ebbd49

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.

source=pdf_text observed=2026-08-07T13:08:36.997337Z digest=sha256:7609364ca5fb80aa1df0f66487862a69599f70c712da19d9b0563f8caafb692c

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.

source=arxiv_source observed=2026-08-07T11:15:44.719192Z digest=sha256:ff2cc612bde958c2657809c7297e1047507260645d0421c4186b9c1704ba0b9e

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.

source=arxiv_source observed=2026-08-07T05:38:26.697578Z digest=sha256:46eea93724330b0bd024a1f41752229ba78df66983fe57c53deb43f836abe604

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.

source=pdf_text observed=2026-08-06T22:08:32.962081Z digest=sha256:35ea58033d19dc59bda50c63cdb0c89c31a6d9f2e4befe23fc04d532ae880290

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.

source=arxiv_source observed=2026-08-05T23:21:25.332133Z digest=sha256:e227a7ca655d228324c78298ef40d3777a6c4e9a5293fb04fc116b43bc70bb24

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.

source=arxiv_source observed=2026-08-05T14:57:24.794551Z digest=sha256:3e823fc224c1f1b3ce46a56e7cbabf999b3c2d839821c9d4028f07ea06cba03b