Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Watermarking Large Language Models and the Generated Content: Opportunities and Challenges
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2410.19096.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T10:30:13.815362Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T22:46:52.943690Z
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 e1ae82ce-eb74-46d2-8372-901a76dfd44c · inbound
SECNEURON: Reliable and Flexible Abuse Control in Local LLMs via Hybrid Neuron Encryption cites this paper.
SECNEURON: Reliable and Flexible Abuse Control in Local LLMs via Hybrid Neuron Encryption Watermarking Large Language Models and the Generated Content: Opportunities and Challenges
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:30:13.815362Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6cfa38ad-b093-44d7-9a92-184f39087d67 · inbound
Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends cites this paper.
Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends Watermarking Large Language Models and the Generated Content: Opportunities and Challenges
Reference 184
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T22:46:52.946817Z
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 9280355c-bdda-4c52-a977-fa62a154e6c6 · inbound
A Comprehensive Dataset for Human vs. AI Generated Text Detection cites this paper.
A Comprehensive Dataset for Human vs. AI Generated Text Detection Watermarking Large Language Models and the Generated Content: Opportunities and Challenges
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T08:03:27.809787Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.