Pith. sign in

Paper Citation Record · LEDGER

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark

As of 9 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2502.08332.

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

pith.paper-citation-record.v1
2502.08332 v2

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T05:40:17.923883Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

  • verified exact1
  • verified fuzzy13
  • unresolved8
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9855e344-1538-4ec4-8c6b-6b45a88a3f48 · outbound

This paper cites On the risk of misinformation pollution with large language models.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark On the risk of misinformation pollution with large language models

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T05:40:18.258480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:40:17.828989Z digest=sha256:85b7374e87ae6f6a5b6b5acee388dac2535c61bc4c6e4f6796a3e5d7f1dabce8

Observation 6a0db3aa-4873-4329-996d-fd68b374bd27 · outbound

This paper cites When LLMs Go Online: The Emerging Threat of Web-Enabled LLMs.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark When LLMs Go Online: The Emerging Threat of Web-Enabled LLMs

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-08T05:40:17.834160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:40:17.834160Z digest=sha256:5478307a4b80944bb84712d05c2424e021eeca739240c191ef200166cd27b442

Observation fffa2ceb-a526-43c9-aaf3-fc1c19d9f16b · outbound

This paper cites Position: On the possibilities of AI-generated text detection.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark Position: On the possibilities of AI-generated text detection

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T05:40:18.244319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:40:17.839410Z digest=sha256:11900fc8e0ad0adc3c3d62e14703f631bb702d8877c8d8b6f88c0d9b94ca56e2

Observation d535924b-85bf-4f22-b7db-5a119dc3416b · outbound

This paper cites Manning, and Chelsea Finn.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark Manning, and Chelsea Finn

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T05:40:18.230292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:40:17.843856Z digest=sha256:0ea4101ac7fee3cdf566856c1ea0a295b4cb95dda11691f43daf2487d15da7c8

Observation 36b280ed-4bc3-43aa-83f3-956d0f4f7a96 · outbound

This paper cites On the Reliability of Watermarks for Large Language Models.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark On the Reliability of Watermarks for Large Language Models

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-08T05:40:17.848544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:40:17.848544Z digest=sha256:127de4d694ed6763bdf626556c24ebee713b17dbfc90cd3cc6d43459cb020754

Observation 3dc2b0be-df78-43f8-9b72-9bb5ad2ef6eb · outbound

This paper cites A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark A Statistical Framework of Watermarks for Large Language Models: Pivot, Detection Efficiency and Optimal Rules

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-08-08T05:40:18.022570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:40:17.853345Z digest=sha256:31e9323f622debe0cd03ae7ea2c3e9fbb9ba81ddf5886a5accf1251a1b9b943a

Observation e8540cfc-97bf-4723-b868-893139cf0282 · outbound

This paper cites A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-08T05:40:17.858739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:40:17.858739Z digest=sha256:1f53fd7a8b1240e9ec258a9ae3ec1a6a13e4894a661234f495bf81daab7ec6bb

Observation 2c8afc7d-5d8f-4750-bf64-eb6136b88f7d · outbound

This paper cites A review of text watermarking: Theory, methods, and applications.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark A review of text watermarking: Theory, methods, and applications

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T05:40:18.215817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:40:17.863414Z digest=sha256:2276c2bc5e9a7fdaab39e8100ebbec5e28f032e99ca30fb1ebe541eacd03c695

Observation 23026bb3-b1c9-440f-9fa6-aa635d3c9975 · outbound

This paper cites Advancing beyond identification: Multi-bit watermark for large language models.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark Advancing beyond identification: Multi-bit watermark for large language models

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T05:40:18.201389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:40:17.867714Z digest=sha256:33f84eb8152a6266cc2346f97c6161b7be39ca0248f6eb511002b3428a3ee1c9

Observation e93f526f-e6de-47f6-9beb-40fff5696292 · outbound

This paper cites Tracing text provenance via context-aware lexical substitution.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark Tracing text provenance via context-aware lexical substitution

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-08T05:40:17.872052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:40:17.872052Z digest=sha256:e4b6a8ebd36e45ed95cfce9a427cffbd93df46b487f375eac8d2bdf19bb1562d

Observation 6e1334b5-4e2f-4487-abcd-6b9df368681d · outbound

This paper cites A watermark for large language models.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark A watermark for large language models

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T05:40:18.177568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:40:17.877180Z digest=sha256:9305f5444e6e05a96f1f26df5f71ae89661090f39d42d1b29f3df1ab549ca0b5

Observation 46568d00-b36c-4aae-986f-319399f2410e · outbound

This paper cites Provable robust watermarking for AI-generated text.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark Provable robust watermarking for AI-generated text

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T05:40:18.163780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:40:17.881283Z digest=sha256:4a92e3ffd3603cdbc37c50c8c3be35a8afaa827e3697370061eb8dd69660cdab

Observation bb05c7ba-9c8e-4cd1-97d9-ac3ee10395d4 · outbound

This paper cites Who wrote this code? watermarking for code generation.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark Who wrote this code? watermarking for code generation

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T05:40:18.149149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:40:17.885432Z digest=sha256:7a793aef78e13cc5b065fc590264e4710c69b43c6aebe609802f11f7b0bc8f9d

Observation 269b4aba-5565-482f-8b08-320e5fdc058a · outbound

This paper cites A resilient and accessible distribution-preserving watermark for large language models.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark A resilient and accessible distribution-preserving watermark for large language models

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T05:40:18.133824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:40:17.889614Z digest=sha256:cb1db97f9a8a44b1436d1baa7082c4b4c6412679847dbff3dfaf0cab54b74631

Observation c2b6c3c7-ea2f-4176-90a2-29a4ad595c91 · outbound

This paper cites Robust distortion-free watermarks for language models.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark Robust distortion-free watermarks for language models

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T05:40:18.119503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:40:17.893672Z digest=sha256:07affe68c7701f2f1c275df5d8eac29d5460ea7a989d695feca055ad82b5efb7

Observation 2fdd92df-cede-4a33-9861-d089a50b62a4 · outbound

This paper cites Undetectable watermarks for language models.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark Undetectable watermarks for language models

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T05:40:18.104657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:40:17.897771Z digest=sha256:aae2a21ee7004a42510968a6b25557ce37a7b84fec5c514a0115fd7eee145e4e

Observation 9915cbe7-38f6-4483-b211-a216c70d03ec · outbound

This paper cites Unbiased watermark for large language models.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark Unbiased watermark for large language models

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T05:40:18.090201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:40:17.901973Z digest=sha256:87e4acf25b187f3be9a3621cf6cb69750dded1ad9275993385c6cc66a67afcc3

Observation 2ed11701-669b-498b-9629-5eda6859599e · outbound

This paper cites No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-08T05:40:17.906275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:40:17.906275Z digest=sha256:fdfed4b8b462271c0608ce12ce6582c70cd91070d76b66a12055a17fbbdb5ec0

Observation 92def736-3462-496f-aece-cec87bde701d · outbound

This paper cites Discovering Spoofing Attempts on Language Model Watermarks.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark Discovering Spoofing Attempts on Language Model Watermarks

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-08T05:40:17.910867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:40:17.910867Z digest=sha256:f231d4d38cc6804cd7fd1bb3c94de8cfc0648eea7da92446c99536891993e50f

Observation c2151a39-b8b8-4699-8e74-a6e3181ab194 · outbound

This paper cites New evaluation metrics capture quality degradation due to llm watermarking, 2023.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark New evaluation metrics capture quality degradation due to llm watermarking, 2023

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T05:40:18.076470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-08T05:40:17.915422Z digest=sha256:107a4512e4690538a922834ef8d5bfa968d1123a14d98376f0bc47aec94b69ea

Observation 17e7ba0c-28a2-4999-9923-96f5040a97f1 · outbound

This paper cites Pubmedqa: A dataset for biomedical research question answering.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark Pubmedqa: A dataset for biomedical research question answering

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-08T05:40:17.919654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:40:17.919654Z digest=sha256:8c9f76a0fa6a1df5a455ae829ddea25d27dabb8b957dcda9647e8460760820e1

Observation 454139dd-f500-41bc-be2c-00cbb73fffa3 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Modification and Generated-Text Detection: Achieving Dual Detection Capabilities for the Outputs of LLM by Watermark OPT: Open Pre-trained Transformer Language Models

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-08T05:40:17.923883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T05:40:17.923883Z digest=sha256:975e18e91135aeeab97bf58fdeeccd3ce5e62489909fcddceb3f5b27c6f6e398

Pith citing papers

No inbound Pith citation observations are available.