Paper Citation Record · LEDGER

Optimizing watermarks for large language models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2312.17295.

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

pith.paper-citation-record.v1
2312.17295 v1

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 2026-08-10T06:31:04.303077+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:57:38.151006Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a603206b-dcd4-4d8f-9db3-60227446676d · inbound

Autoregressive Images Watermarking through Lexical Biasing: An Approach Resistant to Regeneration Attack cites this paper.

Autoregressive Images Watermarking through Lexical Biasing: An Approach Resistant to Regeneration Attack Optimizing watermarks for large language models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T11:57:38.151006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63562bf8-07b1-4485-85f7-133527989681 · inbound

Optimal Multi-bit Generative Watermarking Schemes Under Worst-Case False-Alarm Constraints cites this paper.

Optimal Multi-bit Generative Watermarking Schemes Under Worst-Case False-Alarm Constraints Optimizing watermarks for large language models

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:10:58.086924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 28afb859-af39-4287-a2d6-f02bbd263245 · inbound

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

Trustworthy AI: Ensuring Reliability and Accountability from Models to Agents Optimizing watermarks for large language models

Reference 162

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:21:16.337860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 55d5efa6-24ce-4d2d-9f72-01589465a830 · inbound

SAMark: A Self-Anchored Text Watermarking with Paragraph-Level Paraphrase Robustness cites this paper.

SAMark: A Self-Anchored Text Watermarking with Paragraph-Level Paraphrase Robustness Optimizing watermarks for large language models

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-06-29T21:53:59.620028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4c7aa403-dd52-4571-a6fc-426ca5f055e5 · inbound

SAMark: A Self-Anchored Text Watermarking with Paragraph-Level Paraphrase Robustness cites this paper.

SAMark: A Self-Anchored Text Watermarking with Paragraph-Level Paraphrase Robustness Optimizing watermarks for large language models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T13:13:36.697083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f0b29c3-6ba7-41c7-bf30-675588fc16b2 · inbound

Signature filtering: a lightweight enhancement for statistical watermark detection in large language models cites this paper.

Signature filtering: a lightweight enhancement for statistical watermark detection in large language models Optimizing watermarks for large language models

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-06-27T01:10:19.933934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.