Pith. sign in

Paper Citation Record · LEDGER

Mark Your LLM: Detecting the Misuse of Open-Source Large Language Models via Watermarking

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2503.04636.

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

pith.paper-citation-record.v1
2503.04636 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 2026-08-08T06:32:00.761636+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:30:13.826133Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T22:46:53.049970Z

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 40ae7243-bf1d-47b4-9afa-e382afbd94ea · 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 Mark Your LLM: Detecting the Misuse of Open-Source Large Language Models via Watermarking

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T10:30:13.826133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:30:13.826133Z digest=sha256:f7da8113fb570d4450ba166e9d6eb22e0bb64f700a7952fae1e4cda1b96fe830

Observation de54523a-8a0d-4aa1-9c60-54cd653c7304 · inbound

Towards Provable (In)Secure Model Weight Release Schemes cites this paper.

Towards Provable (In)Secure Model Weight Release Schemes Mark Your LLM: Detecting the Misuse of Open-Source Large Language Models via Watermarking

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-06T23:21:55.794064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:21:55.794064Z digest=sha256:eabffd0f8fbbb326755611e5986a692f9ade01e860d569f1827a38f38935286c

Observation 88d0b193-791c-41f3-b1b3-26dc224a5617 · 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 Mark Your LLM: Detecting the Misuse of Open-Source Large Language Models via Watermarking

Reference 160

Resolution
verified exact
arxiv_id, observed 2026-05-18T22:46:53.052513Z

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.

source=pdf_text observed=2026-05-18T22:45:31.935618Z digest=sha256:f924c2dea8c66e28805e41460a2c15ee80689bd8881625c887e3618076a0afcc

Observation 599eef83-db65-41a6-b1b9-01b61bdc727a · inbound

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

Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption Mark Your LLM: Detecting the Misuse of Open-Source Large Language Models via Watermarking

Reference 56

Resolution
verified exact
arxiv_id, observed 2026-05-18T05:25:54.374933Z

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.

source=pdf_text observed=2026-05-18T05:24:25.622071Z digest=sha256:401946fd80ccbe5d123601b7896d815561400038565ecf2648d9d2c0a3d28ee9

Observation 91b27c8d-b3e3-44dd-8dbe-6e028b93206e · inbound

PathMark: Protecting Intellectual Property of Mixture-of-Expert LLMs via Path Watermarks cites this paper.

PathMark: Protecting Intellectual Property of Mixture-of-Expert LLMs via Path Watermarks Mark Your LLM: Detecting the Misuse of Open-Source Large Language Models via Watermarking

Reference 42

Resolution
unresolved
no resolver link, observed 2026-07-12T00:40:49.755070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T00:40:49.755070Z digest=sha256:340f6e2f0323c501e5b48351a729b25355ea24f64700f6a36beb388f5ba1502e