Paper Citation Record · LEDGER

Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2311.04378.

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

pith.paper-citation-record.v1
2311.04378 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:16:32.874082Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T19:52:35.710151Z

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 d39fae9d-b4f0-4fba-b081-2f23ebfff165 · inbound

Can AI-Generated Text be Reliably Detected? cites this paper.

Can AI-Generated Text be Reliably Detected? Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:29:45.428950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3375c4f7-b084-4f04-936b-2f322d89fddb · inbound

Debiasing Watermarks for Large Language Models via Maximal Coupling cites this paper.

Debiasing Watermarks for Large Language Models via Maximal Coupling Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T18:57:30.329284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0763a66d-3837-46f0-aac2-cdaa24289832 · inbound

CLUE-MARK: Watermarking Diffusion Models using CLWE cites this paper.

CLUE-MARK: Watermarking Diffusion Models using CLWE Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T18:43:14.588679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b8c40f3-7025-467c-abf9-4d032cf5a2ec · inbound

SoK: On the Role and Future of AIGC Watermarking in the Era of Gen-AI cites this paper.

SoK: On the Role and Future of AIGC Watermarking in the Era of Gen-AI Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T18:31:34.200889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 153a0a1d-4dee-450d-bc8d-b56fdb579ecf · inbound

SoK: Watermarking for AI-Generated Content cites this paper.

SoK: Watermarking for AI-Generated Content Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-12T11:13:54.770025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1072f48e-899a-4422-b982-516b8962f626 · inbound

RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models cites this paper.

RAG-WM: An Efficient Black-Box Watermarking Approach for Retrieval-Augmented Generation of Large Language Models Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-10T21:18:38.169092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81926774-9839-4836-95dc-80301806f43a · inbound

GaussMark: A Practical Approach for Structural Watermarking of Language Models cites this paper.

GaussMark: A Practical Approach for Structural Watermarking of Language Models Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Reference 108

Resolution: unresolved; no resolver link, observed 2026-08-10T19:12:46.840367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation deace7cb-de1c-480e-8bd0-393eabe86080 · inbound

Revealing Weaknesses in Text Watermarking Through Self-Information Rewrite Attacks cites this paper.

Revealing Weaknesses in Text Watermarking Through Self-Information Rewrite Attacks Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T23:16:32.874082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff03bd13-9843-40ca-9ca0-cab455922e79 · inbound

LLM Encoder vs. Decoder: Robust Detection of Chinese AI-Generated Text with LoRA cites this paper.

LLM Encoder vs. Decoder: Robust Detection of Chinese AI-Generated Text with LoRA Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T13:22:08.299647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b0c8dcb-5b62-419d-bbe2-f92801e249a4 · inbound

The Coding Limits of Robust Watermarking for Generative Models cites this paper.

The Coding Limits of Robust Watermarking for Generative Models Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T17:16:39.628368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f300bda9-852f-4e97-9f24-724d45906f96 · inbound

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

Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Reference 67

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T05:25:54.489830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6b16228f-7b40-4e2f-a970-c602b72b0a62 · inbound

Robust AI Security and Alignment: A Sisyphean Endeavor? cites this paper.

Robust AI Security and Alignment: A Sisyphean Endeavor? Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Reference 11

Resolution: malformed identifier; arxiv_id, observed 2026-05-16T22:58:38.501070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 499f9394-c31a-4188-935b-8fff8a5188ad · inbound

Haiku to Opus in Just 10 bits: LLMs Unlock Massive Compression Gains cites this paper.

Haiku to Opus in Just 10 bits: LLMs Unlock Massive Compression Gains Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Reference 3

Resolution: malformed identifier; arxiv_id, observed 2026-05-16T05:27:23.130645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 40b94799-3d99-4c46-86b8-4ea1e03b7688 · inbound

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

SLAM: Structural Linguistic Activation Marking for Language Models Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T18:21:07.022032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 204723f0-eb79-4f36-baf6-45cd7a883cb0 · inbound

Who Owns This Agent? Tracing AI Agents Back to Their Owners cites this paper.

Who Owns This Agent? Tracing AI Agents Back to Their Owners Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-20T17:28:47.917633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dc18de17-d2da-4c34-9af1-5324ee13db4f · inbound

From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI cites this paper.

From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Reference 154

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T18:08:50.636719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 13c283a4-7052-4675-808a-d60cc0e81196 · inbound

Authenticity Debt and the Synthetic Content Threat Landscape: A Layered Framework for Trust, Provenance, and IP Governance in the Generative AI Era cites this paper.

Authenticity Debt and the Synthetic Content Threat Landscape: A Layered Framework for Trust, Provenance, and IP Governance in the Generative AI Era Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T19:52:35.712448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.