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Paper Citation Record · LEDGER

Linguistics-Aware Non-Distortionary LLM Watermarking

As of 9 August 2026, this Paper Citation Record lists 100 of 120 outbound references and 0 inbound Pith citation observations for arXiv:2606.00613.

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

pith.paper-citation-record.v1
2606.00613 v1

Coverage vector

measured 100 of 120 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T19:17:26.192208Z

measured 100 of 100 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

100 of 120 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved96
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fc636a13-dee9-4709-a3a5-feb34ef3a288 · outbound

This paper cites Exaone 3.5: Series of large lan- guage models for real-world use cases.

Linguistics-Aware Non-Distortionary LLM Watermarking Exaone 3.5: Series of large lan- guage models for real-world use cases

Reference 1

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arxiv_id, observed 2026-06-28T19:22:34.638765Z

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

source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:6652e356a22f0a670367169dbfd13c6b74f00d4602be5d08f2e60c85184c68c5

Observation adf7a035-18df-46a8-9124-7ca44ec85bec · outbound

This paper cites Procedia Computer Science , year=.

Linguistics-Aware Non-Distortionary LLM Watermarking Procedia Computer Science , year=

Reference 2

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:72105ba4cea917e963c42de9f433bfadd9ddafe9b7f670af453a37c8fb4a3e46

Observation 98bdb772-dcfa-4a84-8763-3328afbbd33d · outbound

This paper cites an unresolved cited work.

Linguistics-Aware Non-Distortionary LLM Watermarking Unresolved cited work

Reference 3

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:29196eef0eb1478e76dcba96e0d2d1da2a9c30dec890ab3326fb19ac31842dce

Observation c252020c-375f-410c-abb9-018a35d3d494 · outbound

This paper cites and Nguyen, Thien Huu.

Linguistics-Aware Non-Distortionary LLM Watermarking and Nguyen, Thien Huu

Reference 4

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:b3ad6d2abf888acd76e5096822f0e50b6d6602307988acb370e7dd3c66394bf2

Observation baf8952d-a8d5-46d3-bf27-66eef675e6d0 · outbound

This paper cites an unresolved cited work.

Linguistics-Aware Non-Distortionary LLM Watermarking Unresolved cited work

Reference 5

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:5c35ef674ac6429582440f5178edbeb3f2c2b719821f2b08fcb767a214573077

Observation de31a8d0-9062-4da5-9a98-82ff2d6f3596 · outbound

This paper cites Saiful and Mubasshir, Kazi and Li, Yuan-Fang and Kang, Yong-Bin and Rahman, M.

Linguistics-Aware Non-Distortionary LLM Watermarking Saiful and Mubasshir, Kazi and Li, Yuan-Fang and Kang, Yong-Bin and Rahman, M

Reference 6

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:364d5048a10bf319ef6870426dd392383d5511532afe41cc2e4f2caecab28979

Observation 25cd852a-e3e0-4a5e-bb6f-66dc582be212 · outbound

This paper cites MLSUM : The Multilingual Summarization Corpus.

Linguistics-Aware Non-Distortionary LLM Watermarking MLSUM : The Multilingual Summarization Corpus

Reference 7

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:a0dcf37dc66c738653980c3717e6de595377398f26d827042ec9a065455484cf

Observation 640a0154-f01a-4baf-93ba-b3aa62696c48 · outbound

This paper cites S udachi: a J apanese T okenizer for B usiness.

Linguistics-Aware Non-Distortionary LLM Watermarking S udachi: a J apanese T okenizer for B usiness

Reference 8

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:18c02b0d6b6711a11e89d0f184c1cebd222e9e645798b2700241dcb0f36b6644

Observation c01cd6a7-9807-4a39-8842-696f9dfd517b · outbound

This paper cites CAM e L Tools: An Open Source Python Toolkit for A rabic Natural Language Processing.

Linguistics-Aware Non-Distortionary LLM Watermarking CAM e L Tools: An Open Source Python Toolkit for A rabic Natural Language Processing

Reference 9

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:5a8684ead0c481b10fe982c5b4283792fdcf065d1afacc63a37b48db45bd9f06

Observation 4f71925f-2271-46f8-b963-e19a76d9fd7d · outbound

This paper cites 2026 , note =.

Linguistics-Aware Non-Distortionary LLM Watermarking 2026 , note =

Reference 10

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:c9c5b83cd4a1f2414ffee3333658f09d3a260879033aa831c1b1e960735a53ff

Observation ff086f24-4303-42c1-be73-1dcf4efe6d92 · outbound

This paper cites G umbel S oft: Diversified Language Model Watermarking via the G umbel M ax-trick.

Linguistics-Aware Non-Distortionary LLM Watermarking G umbel S oft: Diversified Language Model Watermarking via the G umbel M ax-trick

Reference 11

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:31d712554cb9020d654ce5020fb44cdeb29481f4d57529a2d5fa57ad100660e9

Observation fd4c8ee1-5ae4-495e-a59a-1104e500a0ce · outbound

This paper cites an unresolved cited work.

Linguistics-Aware Non-Distortionary LLM Watermarking Unresolved cited work

Reference 12

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:75d283666fdf7da1e92bc2b40c9bb00260bf47e92daa9fecbc7ca22d028e78c0

Observation 3f29ab47-ebce-4693-a002-e033f312a3ce · outbound

This paper cites Watermarking.

Linguistics-Aware Non-Distortionary LLM Watermarking Watermarking

Reference 13

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:2a91258d71e68304abae295908e333a1b2bee8af6cc56eb6af00832c7bd0c576

Observation 7b58318b-9f6b-40dd-a6a1-174962327348 · outbound

This paper cites Proceedings of the 37th Conference on Learning Theory (COLT) , year =.

Linguistics-Aware Non-Distortionary LLM Watermarking Proceedings of the 37th Conference on Learning Theory (COLT) , year =

Reference 14

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:4f52eb174b6866d276c1dc560db8d541ccf17b69c38caaf2eb4ceff241ab281d

Observation d7abb966-f5ed-4847-a687-bb3ea5f7a499 · outbound

This paper cites A Confederacy of Models: a Comprehensive Evaluation of LLM s on Creative Writing.

Linguistics-Aware Non-Distortionary LLM Watermarking A Confederacy of Models: a Comprehensive Evaluation of LLM s on Creative Writing

Reference 15

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:bc31459d8172bfa3d45a750d9fc8a2d71a344c5552c6c950666c7798d4c0ebcc

Observation b8f1e999-689c-4647-994c-7b1a98a9636b · outbound

This paper cites Assisting in Writing W ikipedia-like Articles From Scratch with Large Language Models.

Linguistics-Aware Non-Distortionary LLM Watermarking Assisting in Writing W ikipedia-like Articles From Scratch with Large Language Models

Reference 16

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:4c2ebdb2bc99985229e746df4108f8dd3ac10d224778808a91a756240c8db91a

Observation 13871ab1-b258-4d6e-bb90-7c0dc6545102 · outbound

This paper cites Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering.

Linguistics-Aware Non-Distortionary LLM Watermarking Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering

Reference 17

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:160a9795b1386053ae2138f1353689c535362af9ed7c4dd50be492adc2b5b3e9

Observation 9a9ecfa2-3d26-4163-b3d9-54360fe8ce7f · outbound

This paper cites B io M istral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains.

Linguistics-Aware Non-Distortionary LLM Watermarking B io M istral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains

Reference 18

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:57a38756968fb3913b87f7760540dbfb4916e2a99c1aab2c741b79b2f28b0eaa

Observation 81d784a1-c3d3-43d3-b5c3-8f41e9b75d3d · outbound

This paper cites On Large Language Models' Hallucination with Regard to Known Facts.

Linguistics-Aware Non-Distortionary LLM Watermarking On Large Language Models' Hallucination with Regard to Known Facts

Reference 19

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:7104cef850e8abc10decd2985c9b08ee5d2c36a78de25de7a4c54f6144fe0487

Observation 212c1a3c-c1b6-4e55-8475-03b4fe126e51 · outbound

This paper cites Disinformation Capabilities of Large Language Models.

Linguistics-Aware Non-Distortionary LLM Watermarking Disinformation Capabilities of Large Language Models

Reference 20

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:8987eb646c1ac3529a66982ab633f86fde7467d176929be6c3f63bfb40e7d07f

Observation fe44e018-b48f-48b7-ab57-39566595453e · outbound

This paper cites Proceedings of International Conference on Machine Learning (ICML) , year =.

Linguistics-Aware Non-Distortionary LLM Watermarking Proceedings of International Conference on Machine Learning (ICML) , year =

Reference 21

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:371dda9b7e449fd2bba6586e50a247aa127c135814c529b5328cb27923480cbe

Observation 6ee5515a-4ad5-4c85-b41f-2c5390b909d3 · outbound

This paper cites Proceedings of International Conference on Machine Learning (ICML) , year =.

Linguistics-Aware Non-Distortionary LLM Watermarking Proceedings of International Conference on Machine Learning (ICML) , year =

Reference 22

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:b0e6ab531643880378adde975d90afd8f621cd1527b1e5a2ccfabcffb27f86e0

Observation 2042b922-9ce6-4b07-9ea7-ae7b0d7f1f44 · outbound

This paper cites IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) , year=.

Linguistics-Aware Non-Distortionary LLM Watermarking IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) , year=

Reference 23

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:1e3491317bccba615ef01784307a5452dbb632e53d14e98c96f97e7642d25e2a

Observation b3e6eed6-207a-4b6e-bb6f-a472f6a964f9 · outbound

This paper cites Advances in Neural Information Processing Systems (NeurIPS) , year=.

Linguistics-Aware Non-Distortionary LLM Watermarking Advances in Neural Information Processing Systems (NeurIPS) , year=

Reference 24

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:38d8c9a6a7b93a49cbd7c3e6cc05b8d7f82ce58dfa33f282f7ab82c79954708d

Observation 30e932b3-da6e-4c8e-9a43-482f650bf8fd · outbound

This paper cites Can Large Language Models Be an Alternative to Human Evaluations?.

Linguistics-Aware Non-Distortionary LLM Watermarking Can Large Language Models Be an Alternative to Human Evaluations?

Reference 25

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:978011a0e132bd7234a98410a3b584d6e5f53ec5f5b8c1f09a7a75c2a965bf73

Observation 918ce17a-b119-4b11-9da1-43d1fabdbfef · outbound

This paper cites DITTO : A Spoofing Attack Framework on Watermarked LLM s via Knowledge Distillation.

Linguistics-Aware Non-Distortionary LLM Watermarking DITTO : A Spoofing Attack Framework on Watermarked LLM s via Knowledge Distillation

Reference 26

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:be0950bc6793e758ddbff62b5ba30c7945ad84600fcc630255ce217bf1e38c20

Observation 3f447989-bc5d-4ca6-9cad-d5f59d7ac34b · outbound

This paper cites M ark LLM : An Open-Source Toolkit for LLM Watermarking.

Linguistics-Aware Non-Distortionary LLM Watermarking M ark LLM : An Open-Source Toolkit for LLM Watermarking

Reference 27

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:b2d51a5617f5b3ecbb96da53d14f081cf28a9a5f815306ae343d129c7ca8af6c

Observation 822177fc-855f-4f44-ac7b-f7be4f85c4df · outbound

This paper cites 2024 , howpublished =.

Linguistics-Aware Non-Distortionary LLM Watermarking 2024 , howpublished =

Reference 28

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:c6c657395f9a2c211377f1a2f5185daf216f95679ee9bbca4b110f9aae84f13a

Observation 6eb95b4e-34c1-4c15-8114-ced6844b8eaa · outbound

This paper cites ACM Computing Surveys , year=.

Linguistics-Aware Non-Distortionary LLM Watermarking ACM Computing Surveys , year=

Reference 29

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:0b7ebcf08e51e29e43b6347b9ebb0a52d9f7d1c174e05f2293b3861fc48ce7b2

Observation 9097d9db-8af9-48e4-b358-5e72340061ec · outbound

This paper cites From Intentions to Techniques: A Comprehensive Taxonomy and Challenges in Text Watermarking for Large Language Models.

Linguistics-Aware Non-Distortionary LLM Watermarking From Intentions to Techniques: A Comprehensive Taxonomy and Challenges in Text Watermarking for Large Language Models

Reference 30

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:7ae7ddcf8f3cee8a198f13079bd1e5e6ff1babf927bfe1cd42bc3574fd548e88

Observation 4e953dd6-fed1-4cac-a837-d8cf096b2042 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , year=.

Linguistics-Aware Non-Distortionary LLM Watermarking Proceedings of the AAAI Conference on Artificial Intelligence , year=

Reference 31

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:bfc492666ca631e860eef741b1fa629477ad0aecdfe9ade0a8c50a969fbf326d

Observation ccef5ed0-c96e-40f9-bda2-2f48eaf844b5 · outbound

This paper cites Advances in Neural Information Processing Systems (NeurIPS) , year =.

Linguistics-Aware Non-Distortionary LLM Watermarking Advances in Neural Information Processing Systems (NeurIPS) , year =

Reference 32

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:e087ac5b1df8237ce8341e0ed0b898e208d12cd64dd6cc97bfcd3463141dd694

Observation 80b97dfc-1db0-4d31-aefd-2a0288aaddf2 · outbound

This paper cites International Conference on Machine Learning (ICML) , year=.

Linguistics-Aware Non-Distortionary LLM Watermarking International Conference on Machine Learning (ICML) , year=

Reference 33

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:f79140e2a9341e9beeb32ff54738718fee4d27b23ce87a1a6695dc40d5f54143

Observation fc457bb6-e6b1-422d-9244-545924a705ff · outbound

This paper cites Who Wrote this Code? Watermarking for Code Generation.

Linguistics-Aware Non-Distortionary LLM Watermarking Who Wrote this Code? Watermarking for Code Generation

Reference 34

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:c33365a90dabdca0c66f108e9294e02a4dd7801178c7c411fc1edd1316b5da82

Observation 0e6499f9-bca6-4a28-b3ca-2525ed23b0c8 · outbound

This paper cites An Entropy-based Text Watermarking Detection Method.

Linguistics-Aware Non-Distortionary LLM Watermarking An Entropy-based Text Watermarking Detection Method

Reference 35

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:9920b85f3f8c11278773d51c06a202a236c578e12344697e97e68c42d867cb41

Observation ccfdf35a-b8b9-4e35-913d-91cca6f5b148 · outbound

This paper cites M orph M ark: Flexible Adaptive Watermarking for Large Language Models.

Linguistics-Aware Non-Distortionary LLM Watermarking M orph M ark: Flexible Adaptive Watermarking for Large Language Models

Reference 36

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:01f27e7eddd6f2d7f96de658b9492469513b7a5bdaa595b7e4fa20cdbcaf124c

Observation bc3e631f-b539-44b1-a7ee-fea5a8e0f95c · outbound

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Linguistics-Aware Non-Distortionary LLM Watermarking Unresolved cited work

Reference 37

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:e2763d05be4d6e2fc9ff47a71910a5fe9a06a19203c5c80cd97c43e311c6c7d2

Observation d1376bb1-844f-4a0c-afb6-8b3a77d70b9b · outbound

This paper cites Transactions on Machine Learning Research , year=.

Linguistics-Aware Non-Distortionary LLM Watermarking Transactions on Machine Learning Research , year=

Reference 38

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:caa422a6385d6d18bc6495d513e781cadf47f624f20a2587b0ead8451a16f071

Observation 6f67e685-053a-4b56-a3de-f78e89c6a83a · outbound

This paper cites Nature , year=.

Linguistics-Aware Non-Distortionary LLM Watermarking Nature , year=

Reference 39

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:f25e229d95e1f66a9a3ce761f77798aa8445729cfcc77328c6131c0a099e5141

Observation 8fac1f9f-f505-4509-aaae-ac24f1f5df6b · outbound

This paper cites W at ME : Towards Lossless Watermarking Through Lexical Redundancy.

Linguistics-Aware Non-Distortionary LLM Watermarking W at ME : Towards Lossless Watermarking Through Lexical Redundancy

Reference 40

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:1c583905bd8c02922558804bbca0015dcbd7564e7840644e1b0aec686f1f2551

Observation 620349ac-9f74-4dca-88ed-9941ba009d94 · outbound

This paper cites 1998 , publisher=.

Linguistics-Aware Non-Distortionary LLM Watermarking 1998 , publisher=

Reference 41

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:61de53e0ac546619f5eee90860a343e86f50d8c75d82be490ddb68f2a684d247

Observation 63d21c00-c70f-482e-adc2-954dffb04788 · outbound

This paper cites Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) , year=.

Linguistics-Aware Non-Distortionary LLM Watermarking Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) , year=

Reference 42

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:67fb73b116234bc18b8fc10ebee9c60021c101e6e87922f3b8f179abb400a93d

Observation c0fd6694-1528-40f7-8930-ad815ac7083c · outbound

This paper cites S em S tamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation.

Linguistics-Aware Non-Distortionary LLM Watermarking S em S tamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation

Reference 43

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:93b0c22591c7a45e91d76153b23887937f8a0c7a1248060325f0ec8e91b6f290

Observation a4d3aae1-f3ca-446f-890a-5af2765ba3ec · outbound

This paper cites k- S em S tamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text.

Linguistics-Aware Non-Distortionary LLM Watermarking k- S em S tamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text

Reference 44

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:bc91e41b5f03ce13875fc3b8aedf2f03556a88ff86bb80659755217039749793

Observation 5b448149-660f-4c07-aa7b-c293934365c3 · outbound

This paper cites Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , pages=.

Linguistics-Aware Non-Distortionary LLM Watermarking Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , pages=

Reference 45

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:7b71418d43c01416cd0aaf7bb10a55e553cee539b88c1f00b03c1136b07677b3

Observation 5d290654-f7d5-435a-875b-52270617b3b7 · outbound

This paper cites C ode IP : A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code.

Linguistics-Aware Non-Distortionary LLM Watermarking C ode IP : A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code

Reference 46

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:c70e19150955e1420ff546dce45c43e7cf396d9bf424054206e90829ed4d6b50

Observation 41956ed9-fc56-4e80-a6f3-6cb601436ee6 · outbound

This paper cites Marking Code Without Breaking It: Code Watermarking for Detecting LLM -Generated Code.

Linguistics-Aware Non-Distortionary LLM Watermarking Marking Code Without Breaking It: Code Watermarking for Detecting LLM -Generated Code

Reference 47

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:79a746c3787de7273c55ad645a729bcc5bacce53c891c5f78ca5e7f2ff0a4829

Observation 1bc06b67-580c-487a-bdbf-9b27f3017cd6 · outbound

This paper cites Towards Codable Watermarking for Injecting Multi-Bits Information to.

Linguistics-Aware Non-Distortionary LLM Watermarking Towards Codable Watermarking for Injecting Multi-Bits Information to

Reference 48

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:06f41f9972a7e70a0c3e16aebc2c879bf17a8d4d8af3ba1d713c53872ce09eab

Observation a438c6c3-a5e1-4cc5-a923-4e47484e9a80 · outbound

This paper cites Robust Multi-bit Natural Language Watermarking through Invariant Features.

Linguistics-Aware Non-Distortionary LLM Watermarking Robust Multi-bit Natural Language Watermarking through Invariant Features

Reference 49

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:767469a861e168bb7f3132e3a468d5e543be6797da57323841448945aba263af

Observation e858969b-b784-40aa-bb9f-cc32627998d0 · outbound

This paper cites Advancing Beyond Identification: Multi-bit Watermark for Large Language Models.

Linguistics-Aware Non-Distortionary LLM Watermarking Advancing Beyond Identification: Multi-bit Watermark for Large Language Models

Reference 50

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:fc57c6e12fbf56838c314578e79266eac0a5ee65f2b5117aad702750a641e3d4

Observation 72e82a5d-5d49-47da-a041-6fda957d24b5 · outbound

This paper cites Where Am I From? Identifying Origin of LLM -generated Content.

Linguistics-Aware Non-Distortionary LLM Watermarking Where Am I From? Identifying Origin of LLM -generated Content

Reference 51

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:84f6a72094d37a880fb7a43f6e0e1028185e82560af16bf1e90a093adf2fe664

Observation 8d166c37-01a1-4dbd-929d-240f21b3c6ac · outbound

This paper cites Watermarking Text Generated by Black-Box Language Models.

Linguistics-Aware Non-Distortionary LLM Watermarking Watermarking Text Generated by Black-Box Language Models

Reference 52

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verified exact
arxiv_id, observed 2026-06-28T19:22:34.631423Z

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=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:adbc33e295e26ae088fae4efef8139be50f560cf672662e08c7f1a1d91569039

Observation 36162de1-6253-4946-968d-f79a3360b9f2 · outbound

This paper cites A watermark for black-box language models.arXiv preprint arXiv:2410.02099.

Linguistics-Aware Non-Distortionary LLM Watermarking A watermark for black-box language models.arXiv preprint arXiv:2410.02099

Reference 53

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arxiv_id, observed 2026-06-28T19:22:34.633850Z

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

source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:85dc6efcce7e9c551a6ee05a02ba7037fb25f8b8e867f589163bf307e181ddf2

Observation 04fc5ba8-a524-43f2-8229-4927bb39b6c5 · outbound

This paper cites P ost M ark: A Robust Blackbox Watermark for Large Language Models.

Linguistics-Aware Non-Distortionary LLM Watermarking P ost M ark: A Robust Blackbox Watermark for Large Language Models

Reference 54

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:852691d958bb2dd451fa982101a829505506a6ceeb52e71fe2cef76bb8a4386a

Observation 233f6339-d714-4aa4-a89c-671817257494 · outbound

This paper cites Advances in Neural Information Processing Systems (NeurIPS) , year=.

Linguistics-Aware Non-Distortionary LLM Watermarking Advances in Neural Information Processing Systems (NeurIPS) , year=

Reference 55

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:091900271e42793f29570a0b6f5e68b3d6157eff9b81ef5f2e667e8a8531fed9

Observation e3cb8bdc-28f4-4c35-826e-14585fafd044 · outbound

This paper cites Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation?.

Linguistics-Aware Non-Distortionary LLM Watermarking Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation?

Reference 56

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:49893ade361a686fe4f3ab8a332aec261bfca38ead965c563436f6f9ce107730

Observation 6d928a06-1a59-4be9-8244-b64f4334efab · outbound

This paper cites Can Watermarks Survive Translation? On the Cross-lingual Consistency of Text Watermark for Large Language Models.

Linguistics-Aware Non-Distortionary LLM Watermarking Can Watermarks Survive Translation? On the Cross-lingual Consistency of Text Watermark for Large Language Models

Reference 57

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:0d26280351e1163b150744b1a97e279b35e031d6061f304233624389e415f9df

Observation ab9661c7-d000-4bb8-8080-ba6eaf4aeba5 · outbound

This paper cites Qwen2.5 Technical Report.

Linguistics-Aware Non-Distortionary LLM Watermarking Qwen2.5 Technical Report

Reference 58

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local_arxiv, observed 2026-06-28T19:22:34.636258Z

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:5d4f67b4523420d3b746b5954185cf4319a0c0b30d9cfe52d650d210234502ec

Observation c438ccaf-0a44-4718-9da8-aedb8eb76ac1 · outbound

This paper cites 2024 , url=.

Linguistics-Aware Non-Distortionary LLM Watermarking 2024 , url=

Reference 59

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:9a2bf4aafe7fcd758023f0ae7a49d0a7f7d00ccc303a71ff1d82eaf0c4c07c40

Observation 0ee735a9-da64-49b8-8930-6a87f96334e2 · outbound

This paper cites A Survey on Detection of LLM s-Generated Content.

Linguistics-Aware Non-Distortionary LLM Watermarking A Survey on Detection of LLM s-Generated Content

Reference 60

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:76819a22e6b9019065d0f1cc8a02726a8c2567f90ffcdb86cb550af238504131

Observation c6300411-0995-41b2-9f04-4b5950c1fad9 · outbound

This paper cites Smaller Language Models are Better Zero-shot Machine-Generated Text Detectors.

Linguistics-Aware Non-Distortionary LLM Watermarking Smaller Language Models are Better Zero-shot Machine-Generated Text Detectors

Reference 61

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:511251a1c72c459153c313f4e22a320aa4487f6969aa0038b3144ac2fb7ecb6f

Observation 931ea1e7-d717-4a8c-84ba-341c4e15991a · outbound

This paper cites Beat LLMs at Their Own Game: Zero-Shot LLM -Generated Text Detection via Querying ChatGPT.

Linguistics-Aware Non-Distortionary LLM Watermarking Beat LLMs at Their Own Game: Zero-Shot LLM -Generated Text Detection via Querying ChatGPT

Reference 62

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:54d6241ec3ffa1f77a52632986778604426243f531b1ccd23790799ef45365f5

Observation a42240d9-896d-427c-9e34-53dc49c8f4f0 · outbound

This paper cites DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text.

Linguistics-Aware Non-Distortionary LLM Watermarking DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text

Reference 63

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:5518247ed44ce7f66539a003a2aaec2c24d5a949f4cc04278241b39a58d57619

Observation c247d53c-d199-49b2-bcd6-1cea90ba8c23 · outbound

This paper cites an unresolved cited work.

Linguistics-Aware Non-Distortionary LLM Watermarking Unresolved cited work

Reference 64

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:43b7be3b39f4ec375875015bd506ca9ddfe605a23f37b5ebc78e794190c3c2dc

Observation 7c6ff739-304f-4a19-9d69-97cc7428baf1 · outbound

This paper cites 2020 , url=.

Linguistics-Aware Non-Distortionary LLM Watermarking 2020 , url=

Reference 65

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:650d918cc7c93a38eafb2607432132ce1c667f13b8ee60099ff6e391118ee67c

Observation 981cb47a-255f-4c68-bdea-9904bce8ad2f · outbound

This paper cites The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer Encoders.

Linguistics-Aware Non-Distortionary LLM Watermarking The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer Encoders

Reference 66

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:76f5a166d7b777998b50c82ec102e6f77e9350f8c77e66a0e70fe84b4ed0aa52

Observation 7729df9c-a8f5-4c5a-9b94-1fa427c801de · outbound

This paper cites K at F ish N et: Detecting LLM -Generated K orean Text through Linguistic Feature Analysis.

Linguistics-Aware Non-Distortionary LLM Watermarking K at F ish N et: Detecting LLM -Generated K orean Text through Linguistic Feature Analysis

Reference 67

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:33f40b3a0bedac060867e6985601edf9cecc539ca32ca0939c84502d660e9f4c

Observation 854ba1b1-da7e-4fe4-9ce5-4a8ed7019987 · outbound

This paper cites Korean Journal of Digital Humanities , year=.

Linguistics-Aware Non-Distortionary LLM Watermarking Korean Journal of Digital Humanities , year=

Reference 68

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:362e205ad3d4cb1f9e0044e717d18da7deffd5890a6909782befc9b0fefcc8ff

Observation 395c2111-d255-4996-ae09-246930bf6d6a · outbound

This paper cites and Cho, Sungzoon , booktitle=.

Linguistics-Aware Non-Distortionary LLM Watermarking and Cho, Sungzoon , booktitle=

Reference 69

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:6e9910f7b2a48e127d98bd628e1e11388ecaa634527e6a0550c23cb6bf926796

Observation 056e2fe7-bea7-4a92-9a81-30360b121614 · outbound

This paper cites First Conference on Language Modeling (COLM) , year=.

Linguistics-Aware Non-Distortionary LLM Watermarking First Conference on Language Modeling (COLM) , year=

Reference 70

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:deefc999fae2f2781cc6e3dcc917dbb226117f868f9c5785d08d9d5659bad9c6

Observation b3f49360-8f9d-45de-ab02-4a67b684add3 · outbound

This paper cites Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL).

Linguistics-Aware Non-Distortionary LLM Watermarking Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL)

Reference 71

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:1302a9bc33afca0c0c97106a2ed47c60326fef701999d98fdf2ef9f5be90722d

Observation 06abfc54-53a4-4956-98be-7b969914fda5 · outbound

This paper cites K orean Language Modeling via Syntactic Guide.

Linguistics-Aware Non-Distortionary LLM Watermarking K orean Language Modeling via Syntactic Guide

Reference 72

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:7edbfd9c7dcf816256dde42fe10bbf7e034bedc6b9923b611c715404a797aee5

Observation 80dd2ca0-e6eb-49a3-b33d-e26934492362 · outbound

This paper cites Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on K orean.

Linguistics-Aware Non-Distortionary LLM Watermarking Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on K orean

Reference 73

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:cd5a6fd383386d2ebbe9291bc7dabb6625e95a67ff7cf9f07d188db2b3db24df

Observation 42c7f69a-cf24-4b10-8c3d-93213e1dbb0a · outbound

This paper cites an unresolved cited work.

Linguistics-Aware Non-Distortionary LLM Watermarking Unresolved cited work

Reference 74

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:789ffa42d3059aa3f8fa5240faf19559ea6075c0875b9b3e9a9e4afd5350d724

Observation dc449955-b57d-4f06-a11e-5326baff69f7 · outbound

This paper cites B LEU: a Method for Automatic Evaluation of Machine Translation.

Linguistics-Aware Non-Distortionary LLM Watermarking B LEU: a Method for Automatic Evaluation of Machine Translation

Reference 75

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:eca405e655e16fe9b418d42c68f34d4a72ef6bca8a211262462677ed95de7bb5

Observation 10ebad2a-e332-4440-8567-7eece96c9dfe · outbound

This paper cites ROUGE : A Package for Automatic Evaluation of Summaries.

Linguistics-Aware Non-Distortionary LLM Watermarking ROUGE : A Package for Automatic Evaluation of Summaries

Reference 76

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:88c3d6ae486ef61a46b4409f992a9a5463df16d69ed25100505426d4931952bd

Observation dc7f1fb3-81a1-4a1f-ae5b-dceb96cc3c06 · outbound

This paper cites 1989 , publisher=.

Linguistics-Aware Non-Distortionary LLM Watermarking 1989 , publisher=

Reference 77

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:97b8ba334e839e260284bbd4a3394e6e07f5c8be33e07a24e7200801b6201e3a

Observation 59ee4ed8-66f7-4177-967e-e30ae4482b81 · outbound

This paper cites 1981 , publisher=.

Linguistics-Aware Non-Distortionary LLM Watermarking 1981 , publisher=

Reference 78

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:7bfe2b16e9b7626b365a08cee532d78b2ce026b53178449170372539c5ffd0f3

Observation 99b8801d-6e21-4282-99a4-8157f6037040 · outbound

This paper cites 2001 , publisher=.

Linguistics-Aware Non-Distortionary LLM Watermarking 2001 , publisher=

Reference 79

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:981771e4704bfb8480f77cbdce92b0c618c558d9a18fe437d4203dd6959c81a2

Observation d70d11da-92c8-4dff-ada1-7ad70b746721 · outbound

This paper cites an unresolved cited work.

Linguistics-Aware Non-Distortionary LLM Watermarking Unresolved cited work

Reference 80

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:33301f2c6052b5143516255e6bde9591a0e6fbd501a8a076b58a10f6f960e4f9

Observation a021513c-21a9-4e6f-956d-106a5629a3a2 · outbound

This paper cites an unresolved cited work.

Linguistics-Aware Non-Distortionary LLM Watermarking Unresolved cited work

Reference 81

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:6a893bde4fa88070476d22c0071fceeaa673f0e577f868d2216abbb48a39c069

Observation 36730a2c-efbd-4b6a-925c-a1ae791e086e · outbound

This paper cites Proceedings of the 41st International Conference on Machine Learning (ICML) , year =.

Linguistics-Aware Non-Distortionary LLM Watermarking Proceedings of the 41st International Conference on Machine Learning (ICML) , year =

Reference 82

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:726c11e4f6c19c9ffdba5d749e232b5191d1d78eb3c4ae16982abf1b2c5baff2

Observation 5a8f2364-c4bb-4ad5-b6ca-bbb3e381b915 · outbound

This paper cites Proceedings of the 41st International Conference on Machine Learning (ICML) , year =.

Linguistics-Aware Non-Distortionary LLM Watermarking Proceedings of the 41st International Conference on Machine Learning (ICML) , year =

Reference 83

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:96ee79ae837caa8be7d1bea1ad514992b2e133af53ddd89f8cae51cbc8f8eb77

Observation 74800e4f-8bc3-490b-95a5-30110dd9795c · outbound

This paper cites Improved Unbiased Watermark for Large Language Models.

Linguistics-Aware Non-Distortionary LLM Watermarking Improved Unbiased Watermark for Large Language Models

Reference 84

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:f18e7a4cb556109fa8195e5ba75e84161775130d4faa9089952a80c745f2dd72

Observation ab00b5d0-a856-4fce-8253-97ed27e99585 · outbound

This paper cites Invisible Entropy: Towards Safe and Efficient Low-Entropy LLM Watermarking.

Linguistics-Aware Non-Distortionary LLM Watermarking Invisible Entropy: Towards Safe and Efficient Low-Entropy LLM Watermarking

Reference 85

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:d0c776b61e4d7addc93e97d7c97320a0e5b3b838627ba6ad8c9ceee1036f28b2

Observation d715aa95-361d-47fb-a334-d25edc910a04 · outbound

This paper cites MULTIT u DE : Large-Scale Multilingual Machine-Generated Text Detection Benchmark.

Linguistics-Aware Non-Distortionary LLM Watermarking MULTIT u DE : Large-Scale Multilingual Machine-Generated Text Detection Benchmark

Reference 86

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:73b295776d445f052d4f02799b3dbd1490507bd56eac24c26c81236161d1f4f3

Observation c5bf3134-1bef-4c5b-b591-ff036b324b48 · outbound

This paper cites an unresolved cited work.

Linguistics-Aware Non-Distortionary LLM Watermarking Unresolved cited work

Reference 87

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:490c1e662c0f9ec120380bd9468a720faa1306a5a828efdf6c69d5c008bc1f6d

Observation 8f6166b0-150c-4c26-871f-8f343136431f · outbound

This paper cites Yu , booktitle=.

Linguistics-Aware Non-Distortionary LLM Watermarking Yu , booktitle=

Reference 88

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:0c5897a8757abab1f1b46783163f7805689403748ec045b1dc15f465f7622d67

Observation 5068c768-a050-4b7b-a2e3-6eb66a1a5b65 · outbound

This paper cites an unresolved cited work.

Linguistics-Aware Non-Distortionary LLM Watermarking Unresolved cited work

Reference 89

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:55131410c533bd2e96c2d4689c0f4cb9a91f23a315b01e694d2e75711f519c09

Observation e467f337-66ef-43d4-bae0-ce1d6d411c1c · outbound

This paper cites an unresolved cited work.

Linguistics-Aware Non-Distortionary LLM Watermarking Unresolved cited work

Reference 90

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:85f9f2fa71e057400941fdafde999370224fc15bc6419754b86dbb0986542bc5

Observation 8d42d02a-7b9f-4fbb-9a63-19b16b1fe582 · outbound

This paper cites Revisiting the Robustness of Watermarking to Paraphrasing Attacks.

Linguistics-Aware Non-Distortionary LLM Watermarking Revisiting the Robustness of Watermarking to Paraphrasing Attacks

Reference 91

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:0694ee1fcf732689cf5bc5e1a41f92b54e087c4c621076187ac4e046f259f05f

Observation e03ba73b-717d-434a-b939-fa8ccc6e2852 · outbound

This paper cites W ater B ench: Towards Holistic Evaluation of Watermarks for Large Language Models.

Linguistics-Aware Non-Distortionary LLM Watermarking W ater B ench: Towards Holistic Evaluation of Watermarks for Large Language Models

Reference 92

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:0d9b3747a59ea955d12bb59eb2bafc11de4052b5e104fe5400439267a6f587b6

Observation 348b94e5-0453-425f-ab62-f5c78fcb8414 · outbound

This paper cites S tanza: A Python Natural Language Processing Toolkit for Many Human Languages.

Linguistics-Aware Non-Distortionary LLM Watermarking S tanza: A Python Natural Language Processing Toolkit for Many Human Languages

Reference 93

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:c635b8301dd514f9f8a55e72a45e13e6ccd0e3087c786cd89b9d56fcfa87a1e8

Observation 2c6bd202-3db2-4473-a46e-0ecb6ed2acfd · outbound

This paper cites Watermarking Large Language Models: An Unbiased and Low-risk Method.

Linguistics-Aware Non-Distortionary LLM Watermarking Watermarking Large Language Models: An Unbiased and Low-risk Method

Reference 94

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:c5c4cfd075b74dc0144480f2f8b784e4d5a61414e61d48bcbe82f8e889123369

Observation 3c8ee6ed-cea6-406f-921a-8e5ca22b1f4e · outbound

This paper cites Watermark under Fire: A Robustness Evaluation of LLM Watermarking.

Linguistics-Aware Non-Distortionary LLM Watermarking Watermark under Fire: A Robustness Evaluation of LLM Watermarking

Reference 95

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:850518977d6fb5a92a2d0dfa1d761dfa9f5e0f3ca920256804646d4a67dbfb93

Observation a23daf27-f39c-450d-b478-53f4538479e1 · outbound

This paper cites Evaluating the Robustness and Accuracy of Text Watermarking Under Real-World Cross-Lingual Manipulations.

Linguistics-Aware Non-Distortionary LLM Watermarking Evaluating the Robustness and Accuracy of Text Watermarking Under Real-World Cross-Lingual Manipulations

Reference 96

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:856c827597ec4fe67ad044ea72b7775cd48c9855302b38a7699e11bfadb4ac12

Observation f0333ede-ca2b-4b61-92a7-e58dc373473e · outbound

This paper cites an unresolved cited work.

Linguistics-Aware Non-Distortionary LLM Watermarking Unresolved cited work

Reference 97

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:a1fafbdd791531d82837dd8ef849798a18f8d60c2d11aa572dadf5f47f82ce1c

Observation ce34972d-e65d-4589-a573-4718aa0f1b27 · outbound

This paper cites 1989 , publisher=.

Linguistics-Aware Non-Distortionary LLM Watermarking 1989 , publisher=

Reference 98

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:8b6b87467cad6d678cdb78b675cc78316021e6e00d81b8bcedf8e07332f09f2f

Observation 5d9e50ef-8028-4594-8ce8-c88fc34fb7d7 · outbound

This paper cites Universals of language , year=.

Linguistics-Aware Non-Distortionary LLM Watermarking Universals of language , year=

Reference 99

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:d3f5878e98f3ae61c876645b686b539496013449d3d350cd606696724297481e

Observation 7f6cf803-75e4-4eb7-8584-318c6249d223 · outbound

This paper cites 2005 , publisher=.

Linguistics-Aware Non-Distortionary LLM Watermarking 2005 , publisher=

Reference 100

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source=arxiv_source observed=2026-06-28T19:17:26.192208Z digest=sha256:fbf3e4567c31b8fe04ece0d5235581681cc6b3afc7cc422e47638adc7344db30

Pith citing papers

No inbound Pith citation observations are available.