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Paper Citation Record · LEDGER

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

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 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 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:49:58.584755Z

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.

source=pdf_text observed=2026-08-11T22:49:58.584755Z digest=sha256:9922c738883574e1c8b970c7906b0b260af402e6d117099dbcd89cff7f4b5fc4

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.

source=pdf_text observed=2026-08-04T22:33:34.172710Z digest=sha256:467565f74daf72eaa7668168cde2c0575ed98502c031e31bc951db3218c29ec9

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.

source=pdf_text observed=2026-08-04T05:52:05.468550Z digest=sha256:22238c77c406cbda8631658c9d208532f1e7ec47165b31fd621642400b1bae03

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 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-11T00:53:42.185498Z digest=sha256:182f9f8e628e622376218a41828ce6aa78932bd6b5129407848706d6b5795688

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.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:e6b9cc515e900f5e8b54fcad65f201f44c9bb630d7a2602b01c0c37909713fd2