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Paper Citation Record · LEDGER

REMARK-LLM: A Robust and Efficient Watermarking Framework for Generative Large Language Models

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.12362.

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

pith.paper-citation-record.v1
2310.12362 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:58:01.039218Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T22:46:52.960462Z

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 fcdf0732-11c3-40d4-98ca-c8b48ad23a4a · inbound

SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text cites this paper.

SEFD: Semantic-Enhanced Framework for Detecting LLM-Generated Text REMARK-LLM: A Robust and Efficient Watermarking Framework for Generative Large Language Models

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-12T18:58:01.039218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:58:01.039218Z digest=sha256:e953212167c1f91b29acd9865be01e8ea1ac535a0aa9d42bee714054cba3ba2d

Observation 00706fc2-63b6-4d07-a545-0c75da7dc40e · 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 REMARK-LLM: A Robust and Efficient Watermarking Framework for Generative Large Language Models

Reference 182

Resolution
verified exact
arxiv_id, observed 2026-05-18T22:46:52.963377Z

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.

source=pdf_text observed=2026-05-18T22:45:31.935618Z digest=sha256:b0abf56fd736a13640fafb7cc4c1a6d271a989e5adba633e3164789f7ec4c543