Paper Citation Record · LEDGER

Optimizing Adaptive Attacks against Watermarks for Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2410.02440.

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

pith.paper-citation-record.v1
2410.02440 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 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T14:40:54.335560Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T05:27:05.410942Z

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 b723d7a5-186a-44e1-b4c5-633a95445b44 · inbound

Mitigating Watermark Forgery in Generative Models via Randomized Key Selection cites this paper.

Mitigating Watermark Forgery in Generative Models via Randomized Key Selection Optimizing Adaptive Attacks against Watermarks for Language Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-19T05:27:05.414966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 646e5f38-fd0f-418f-b767-8b32c416eb65 · inbound

First-Place Solution to NeurIPS 2024 Invisible Watermark Removal Challenge cites this paper.

First-Place Solution to NeurIPS 2024 Invisible Watermark Removal Challenge Optimizing Adaptive Attacks against Watermarks for Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T14:40:54.335560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4203f82-67a2-4a9d-b65b-e44bdc7a28af · inbound

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

Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption Optimizing Adaptive Attacks against Watermarks for Language Models

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:25:54.428653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 816375f6-b7aa-484b-9df8-095c7c70de76 · inbound

Adaptively Robust LLM Monitoring via Activation Watermarking cites this paper.

Adaptively Robust LLM Monitoring via Activation Watermarking Optimizing Adaptive Attacks against Watermarks for Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-02T17:39:41.676660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 011d15ac-c33d-4349-9ac4-63d679e6b40e · inbound

RLSpoofer: A Lightweight Evaluator for LLM Watermark Spoofing Resilience cites this paper.

RLSpoofer: A Lightweight Evaluator for LLM Watermark Spoofing Resilience Optimizing Adaptive Attacks against Watermarks for Language Models

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:41:06.056229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f1f62c0d-6eb5-40f7-9534-56e390f0d85e · inbound

TimeMark: A Trustworthy Time Watermarking Framework for Exact Generation-Time Recovery from AIGC cites this paper.

TimeMark: A Trustworthy Time Watermarking Framework for Exact Generation-Time Recovery from AIGC Optimizing Adaptive Attacks against Watermarks for Language Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:10:59.135454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.