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Paper Citation Record · LEDGER

DeepTextMark: A Deep Learning-Driven Text Watermarking Approach for Identifying Large Language Model Generated Text

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2305.05773.

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

pith.paper-citation-record.v1
2305.05773 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:43:39.272750Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T22:46:53.070821Z

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 3ef5afb1-9dd0-461d-bfd1-6373981e269e · inbound

Invisible Entropy: Towards Safe and Efficient Low-Entropy LLM Watermarking cites this paper.

Invisible Entropy: Towards Safe and Efficient Low-Entropy LLM Watermarking DeepTextMark: A Deep Learning-Driven Text Watermarking Approach for Identifying Large Language Model Generated Text

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-07T15:43:39.272750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:43:39.272750Z digest=sha256:14a9c75d32490acdf35a82c362948ab7cc09ed936181846245dbe4337463cb7c

Observation 43c68373-22b5-4c47-86e8-eb669950b295 · inbound

Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends cites this paper.

Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends DeepTextMark: A Deep Learning-Driven Text Watermarking Approach for Identifying Large Language Model Generated Text

Reference 107

Resolution
verified exact
arxiv_id, observed 2026-05-18T22:46:53.073485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T22:45:31.935618Z digest=sha256:dbd1283fcb0ae4994181402e0104bfb616a91b155436a155952bac92ed42a67a

Observation 8f8c5357-dc16-48a1-841c-94c58cbdf81f · inbound

Can Watermarking Techniques Help Prevent LLM Model Stealing? cites this paper.

Can Watermarking Techniques Help Prevent LLM Model Stealing? DeepTextMark: A Deep Learning-Driven Text Watermarking Approach for Identifying Large Language Model Generated Text

Reference 72

Resolution
unresolved
no resolver link, observed 2026-07-14T09:13:20.561611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:bb324b9863035214fb8e3138355996bc4d0daaf77808b95a0834250aac5d316c