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Paper Citation Record · LEDGER

Watermarking Techniques for Large Language Models: A Survey

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2409.00089.

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

pith.paper-citation-record.v1
2409.00089 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:30:13.820538Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T04:09:34.592296Z

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 cdcda08a-356b-4496-9ab1-61e08432b8e4 · inbound

SECNEURON: Reliable and Flexible Abuse Control in Local LLMs via Hybrid Neuron Encryption cites this paper.

SECNEURON: Reliable and Flexible Abuse Control in Local LLMs via Hybrid Neuron Encryption Watermarking Techniques for Large Language Models: A Survey

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T10:30:13.820538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:30:13.820538Z digest=sha256:866ae17828dfcbe7a0c2c7c0bd34e69aed57f7a596537a07a31af40bfeb0dfdb

Observation 6b66ec76-4704-4d84-bb8a-678e90af440b · inbound

Watermarking Large Language Model-based Time Series Forecasting cites this paper.

Watermarking Large Language Model-based Time Series Forecasting Watermarking Techniques for Large Language Models: A Survey

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T13:21:26.144007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:21:26.144007Z digest=sha256:b043504aae99034e8fb6df43681c55da2faccd3da65e82c4bb482e9c37fe848a

Observation e02fe4e0-59a6-46d0-84a1-70a0e9acd6d4 · 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 Watermarking Techniques for Large Language Models: A Survey

Reference 94

Resolution
verified exact
arxiv_id, observed 2026-05-18T22:46:53.297861Z

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=sha256:16fe0011cd7fd467bf170b92da2d8b89b8b0ad53b48fdf6578662318b54714d3

Observation c6ac6c94-70ae-42df-8dfb-db2441657320 · inbound

Green-Red Watermarking for Recommender Systems cites this paper.

Green-Red Watermarking for Recommender Systems Watermarking Techniques for Large Language Models: A Survey

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-11T21:26:17.378098Z

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=2026-05-08T05:33:40.420853Z digest=sha256:776e69bc4c93ac3584a8b13d6c720eebbf0a9f7f6b1dedb0126fa71086aa9c9e

Observation 50f5c2c6-18fb-4a73-b8e6-660e80b6ad98 · inbound

PeerCheck: Enhancing LLM-Generated Academic Reviews Towards Human-Level Quality cites this paper.

PeerCheck: Enhancing LLM-Generated Academic Reviews Towards Human-Level Quality Watermarking Techniques for Large Language Models: A Survey

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-07-04T04:09:34.594933Z

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=2026-06-26T17:12:38.192534Z digest=sha256:d4dd8eab466fe25372a09460e3390620ad43e61d72b613178550cdce768fd9d2