Paper Citation Record · LEDGER

PostMark: A Robust Blackbox Watermark for Large Language Models

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2406.14517.

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

pith.paper-citation-record.v1
2406.14517 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:46:38.071385Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T05:25:54.422600Z

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 d6740e87-76ee-4a1e-990b-5b606cba37f7 · inbound

CoheMark: A Novel Sentence-Level Watermark for Enhanced Text Quality cites this paper.

CoheMark: A Novel Sentence-Level Watermark for Enhanced Text Quality PostMark: A Robust Blackbox Watermark for Large Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T10:46:38.071385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fef9b3bd-fcf5-4dc0-8652-758f26024b47 · inbound

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework cites this paper.

Towards Structurally Explainable Machine-Generated Text Detection: A Graph-Perspective Framework PostMark: A Robust Blackbox Watermark for Large Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T20:38:53.682932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5086a683-9c62-4ed6-9ff9-42136f6c745e · inbound

Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption cites this paper.

Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption PostMark: A Robust Blackbox Watermark for Large Language Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:25:54.424728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fe8bd5dd-443a-4030-81ba-b9cf4b608665 · inbound

SWAN: Semantic Watermarking with Abstract Meaning Representation cites this paper.

SWAN: Semantic Watermarking with Abstract Meaning Representation PostMark: A Robust Blackbox Watermark for Large Language Models

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:51:09.576811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ec6ee52a-5f7d-406c-a52d-551e0035d0b1 · inbound

SLAM: Structural Linguistic Activation Marking for Language Models cites this paper.

SLAM: Structural Linguistic Activation Marking for Language Models PostMark: A Robust Blackbox Watermark for Large Language Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:21:07.143132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 604e6349-dd86-4f41-8edb-cb0be9fece28 · inbound

Trustworthy AI: Ensuring Reliability and Accountability from Models to Agents cites this paper.

Trustworthy AI: Ensuring Reliability and Accountability from Models to Agents PostMark: A Robust Blackbox Watermark for Large Language Models

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:21:16.246944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.