Paper Citation Record · LEDGER

Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMs

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2407.04411.

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

pith.paper-citation-record.v1
2407.04411 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 2026-08-22T06:32:14.747728+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:13:34.586634Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T05:00:57.256115Z

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 1dae55d9-880f-4e53-9a28-23b9c51a5205 · inbound

Robust Multi-bit Text Watermark with LLM-based Paraphrasers cites this paper.

Robust Multi-bit Text Watermark with LLM-based Paraphrasers Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMs

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T22:49:58.584755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c650cd6f-508f-4deb-b6fc-bf2b9bef4a2e · inbound

STAMP Your Content: Proving Dataset Membership via Watermarked Rephrasings cites this paper.

STAMP Your Content: Proving Dataset Membership via Watermarked Rephrasings Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMs

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T12:13:34.586634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b95bd14d-b86e-406c-a78c-c2c31246a605 · inbound

Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm cites this paper.

Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMs

Reference 156

Resolution: unresolved; no resolver link, observed 2026-08-04T22:33:34.172710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f81a5b0b-7a8c-4a2e-bf48-512b74991ef5 · inbound

When Only the Final Text Survives: Implicit Execution Tracing for Multi-Agent Auditing cites this paper.

When Only the Final Text Survives: Implicit Execution Tracing for Multi-Agent Auditing Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMs

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-04T05:52:05.468550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7797c44-d2e6-44e5-90ee-1d6a065cf43f · inbound

Dataset Watermarking for Closed LLMs with Provable Detection cites this paper.

Dataset Watermarking for Closed LLMs with Provable Detection Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMs

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:00:57.261229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f5634625-0c6f-4d12-9f95-d43a80ee1172 · inbound

Can Watermarking Techniques Help Prevent LLM Model Stealing? cites this paper.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMs

Reference 68

Resolution: unresolved; no resolver link, observed 2026-07-14T09:13:20.561611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.