Paper Citation Record · LEDGER

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models

As of 20 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2502.05213.

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

pith.paper-citation-record.v1
2502.05213 v2

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:55:02.462850Z

measured 15 of 15 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

verified exact0
verified fuzzy8
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b7dcdbac-eef4-4b75-8676-ddf6a4d9c0e3 · outbound

This paper cites GPT-4 Technical Report.

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T12:55:02.418983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a69a0b3-abf8-4a6c-9e65-ebd0b02b1853 · outbound

This paper cites Model Leeching: An Extraction Attack Targeting LLMs.

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models Model Leeching: An Extraction Attack Targeting LLMs

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T12:55:02.423410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9176d7bf-3dc3-4142-a816-ea759674e0b7 · outbound

This paper cites Can llm-generated misinformation be detected? In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024.

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models Can llm-generated misinformation be detected? In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:55:02.599838Z

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 ccd5852c-69b9-4c7a-9287-acda88700a6b · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T12:55:02.430605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 067ce742-ead2-4e3c-96fb-be4a7705fdcc · outbound

This paper cites Unbiased watermark for large language models, 2023.

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models Unbiased watermark for large language models, 2023

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:55:02.591579Z

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 ef9a2254-89c5-4e0a-a063-96ddb203cb1e · outbound

This paper cites A watermark for large language models.

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models A watermark for large language models

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:55:02.583112Z

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 dd6f2b6a-0c45-46d7-8a31-b7491084cd0f · outbound

This paper cites A semantic invariant robust watermark for large language models, 2024.

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models A semantic invariant robust watermark for large language models, 2024

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T12:55:02.441403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29758d92-a837-4f54-a478-868eb55f86dc · outbound

This paper cites A survey of text watermarking in the era of large language models.

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models A survey of text watermarking in the era of large language models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:55:02.569561Z

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 36d44b3e-0f04-47ab-ae49-49ec26c66f4e · outbound

This paper cites Academic integrity considerations of ai large language models in the post-pandemic era: Chatgpt and beyond.

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models Academic integrity considerations of ai large language models in the post-pandemic era: Chatgpt and beyond

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:55:02.560792Z

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 1a1f1f7a-d64d-4ac9-989a-a00902790a14 · outbound

This paper cites Language models are unsupervised multitask learners.

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models Language models are unsupervised multitask learners

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T12:55:02.449532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab66da2a-267c-4114-808e-c70294fc0ef2 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T12:55:02.452464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d76bb388-a231-40ce-aabb-1f81149ef6e8 · outbound

This paper cites The poisson binomial distribution—old & new.

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models The poisson binomial distribution—old & new

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:55:02.541277Z

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 a2859610-4fad-4135-baae-f823efabca5c · outbound

This paper cites Towards codable watermarking for injecting multi-bits information to llms.

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models Towards codable watermarking for injecting multi-bits information to llms

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:55:02.533091Z

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 10df06c6-c3f7-4aa5-83f6-6c760ec56508 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models OPT: Open Pre-trained Transformer Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-09T12:55:02.460083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef69aaad-0a0f-4f1a-b7f7-a966f481d575 · outbound

This paper cites \ REMARK-LLM \ : A robust and efficient watermarking framework for generative large language models.

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models \ REMARK-LLM \ : A robust and efficient watermarking framework for generative large language models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:55:02.523659Z

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.

Pith citing papers

No inbound Pith citation observations are available.