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Paper Citation Record · LEDGER

Can Watermarking Techniques Help Prevent LLM Model Stealing?

As of 18 August 2026, this Paper Citation Record lists 83 of 83 outbound references and 0 inbound Pith citation observations for arXiv:2607.10794.

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

pith.paper-citation-record.v1
2607.10794 v1

Coverage vector

measured 83 of 83 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T09:13:20.561611Z

measured 83 of 83 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

83 of 83 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved82
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 32439dd5-1e8e-4566-9be9-12d9d79cdb5f · outbound

This paper cites 2020 , url =.

Can Watermarking Techniques Help Prevent LLM Model Stealing? 2020 , url =

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:3c2547f6b8e14959be1821467f5a54534c4e1441a1285abca5822cb7d7d6c49a

Observation 01098838-3887-406a-8648-35fb9353b346 · outbound

This paper cites 2021 , eprint=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? 2021 , eprint=

Reference 2

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:8483086df1d7bcaaafddd77b4c4fba3006737459f23309a6ba88271bf2b784d3

Observation 4aa5f0a6-75dd-4090-803f-8ac81e4347c7 · outbound

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

Can Watermarking Techniques Help Prevent LLM Model Stealing? Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 3

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:2cffe03ac0e7964b73975901253cdb7376523944608353ee9913ce5ea2c6de4a

Observation cdf8c687-e42e-4f62-b02c-ec0ceb02801f · outbound

This paper cites Findings of the Association for Computational Linguistics: EMNLP 2024 , pages=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Findings of the Association for Computational Linguistics: EMNLP 2024 , pages=

Reference 4

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:b14b5640692dfa180b4519676afd79d592d09d220c5710e21122f37605a8f849

Observation 43040dfb-66bb-47ca-9814-d284883ba7ae · outbound

This paper cites International Conference on Machine Learning , pages=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? International Conference on Machine Learning , pages=

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:235e6d945412336b46a5a1bcc3790b93031725ba87c3cea0f971e603d05f07fd

Observation ae7069ef-1ccd-4d77-adec-be00956f5fed · outbound

This paper cites and Tao, Dacheng , title =.

Can Watermarking Techniques Help Prevent LLM Model Stealing? and Tao, Dacheng , title =

Reference 6

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no resolver link, observed 2026-07-14T09:13:20.561611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:773ecf3b08659c7b9135ec417f96d8c7e238de9ee8a07b8ad749d5bfe10d26c8

Observation fdbf999e-f9e6-4545-8dee-e31e3ff36950 · outbound

This paper cites 2018 , publisher=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? 2018 , publisher=

Reference 7

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no resolver link, observed 2026-07-14T09:13:20.561611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:8ad4aafb179b82a2d2ef06b049601a5c465db65aa8cbfacb9cfaf3c39c613799

Observation 75031fb6-a81a-4998-90d6-80ad15a46392 · outbound

This paper cites Proceedings of the Conference on Fairness, Accountability, and Transparency , pages=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Proceedings of the Conference on Fairness, Accountability, and Transparency , pages=

Reference 8

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:35a391f5b02e7ab67a9020fdc1c8e84da5ca82eb961b406b4bb95dd5ad5e03be

Observation 001a393c-459e-44ac-badd-efbbbda51a4f · outbound

This paper cites 29th USENIX security symposium (USENIX Security 20) , pages=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? 29th USENIX security symposium (USENIX Security 20) , pages=

Reference 9

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:a1e3a25c2b51499284907f3dc8622ff9eb17b74287e6aafa0fdc89e647170ccd

Observation bda3d637-303e-4267-b633-1334b2f27873 · outbound

This paper cites 2024 , eprint=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? 2024 , eprint=

Reference 10

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:a03b135cc44d10d7ae81583059857e9816250a509c65ca0e06a9b77e268f5966

Observation 79b07cca-17bd-443b-a9c3-f657fe3f84f4 · outbound

This paper cites 2025 , url =.

Can Watermarking Techniques Help Prevent LLM Model Stealing? 2025 , url =

Reference 11

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:b2f4b0522c95b6c2af2c028a3d2b58237a3ca047dbef3b34af7c42ae6234aa22

Observation 11aa0d90-a7c7-47a2-bb0c-7bd8d7b4d712 · outbound

This paper cites 2023 , eprint=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? 2023 , eprint=

Reference 12

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:69359bfd53b2cf6b8b28d951791d2464cdf356a203a74c466db6ebbfa4052a46

Observation 7f5adf1f-ece9-4acd-9c46-3ffe2213214f · outbound

This paper cites The Llama 3 Herd of Models , journal =.

Can Watermarking Techniques Help Prevent LLM Model Stealing? The Llama 3 Herd of Models , journal =

Reference 13

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:cabbbd23882d14232a47dbaa2c108c0c3a387023c356679aa93b9bf87d68d0bc

Observation 4e584270-0eec-4b88-8275-366b9eae4d15 · outbound

This paper cites Stealing Machine Learning Models via Prediction APIs , booktitle =.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Stealing Machine Learning Models via Prediction APIs , booktitle =

Reference 14

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:795a8477497453e022407a597bf252a648a59f5fd97a04fa63c7df88c6f09c69

Observation 3a379c1a-507b-4212-81b1-0cf8445d0b17 · outbound

This paper cites International Conference on Machine Learning , pages=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? International Conference on Machine Learning , pages=

Reference 16

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:0f55bca3f4961879d4b251a4d40a4f1466d83705c40bbf23167bef2f161ff7b3

Observation 1f70c9b7-7e82-4ee4-8ba7-a0bb4211a199 · outbound

This paper cites an unresolved cited work.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Unresolved cited work

Reference 19

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:1c28c464bbf4d2e8276aca610cf0fb8db9e64a17bfc5fe508f6072565efe2a55

Observation 7799c18a-d9b6-49bc-8c9a-11ecc17ece43 · outbound

This paper cites Annual international cryptology conference , pages=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Annual international cryptology conference , pages=

Reference 20

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:10a1dad1f4992a86489c80bcaf779606de6be125cfac6cecf75e23f2f9a084a9

Observation 1989be27-1d08-4430-bb66-0574b0866c05 · outbound

This paper cites International conference on machine learning , pages=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? International conference on machine learning , pages=

Reference 22

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no resolver link, observed 2026-07-14T09:13:20.561611Z

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:d24bd71ffe5d018adf4dbffe3633d24c2128333ea1adc85c426c94e974b126f6

Observation d7f983f8-b6ca-4637-a21d-2fa1145b7d78 · outbound

This paper cites Annals of Mathematics , volume=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Annals of Mathematics , volume=

Reference 24

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:9260ad9a69da9991d838fb42b0f1fbafc23f135e375b125d19a1c865b8167d45

Observation add78931-7ce2-432e-85b2-8a2f58ffb531 · outbound

This paper cites 2010 , publisher=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? 2010 , publisher=

Reference 25

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no resolver link, observed 2026-07-14T09:13:20.561611Z

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:0a35ce0aa58d362b669830bd2e6898f9d1f5558522d3bf8e2f536aff318731a3

Observation a05680af-25a4-436d-a7fa-b2e3954f2d6e · outbound

This paper cites Mathematische Annalen , volume=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Mathematische Annalen , volume=

Reference 26

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:838c58286b8e766a24d27f1b069b9cdd86f4c53a16c3f8e39e5d29c308bfaa9f

Observation 13800ae3-2bb3-4101-92d9-7e02ad24a95b · outbound

This paper cites Linear Algebra and its Applications , volume=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Linear Algebra and its Applications , volume=

Reference 27

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:fd6fe6a0f3a4774767cf1e6b8428fc47afb0087ac1a6ee9539f258f346b7b5d1

Observation 171e88e0-161d-48f7-a163-3fccbaeab857 · outbound

This paper cites 1998 , publisher=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? 1998 , publisher=

Reference 28

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:4694dc2a96efed18c013449cf53588af70ba782f06924ce6bb429e196abf45e4

Observation 43a70280-159d-4200-8a5b-55550fa91a1e · outbound

This paper cites IEEE Transactions on Acoustics, Speech, and Signal Processing , volume=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? IEEE Transactions on Acoustics, Speech, and Signal Processing , volume=

Reference 29

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:f602f25ec4daf2e3c65876190f0d5255c3a6cfb4f2ec7cdaf9d318f97f3444ce

Observation 58f2b118-1401-4c47-b9cb-c717e7f34b8e · outbound

This paper cites Chemometrics and Intelligent Laboratory Systems , volume=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Chemometrics and Intelligent Laboratory Systems , volume=

Reference 30

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:7c6577e16348d7958447b4f1c7a2deea9d0c31b35c1ec7dcdaa06ca7baaee52e

Observation d0314d2f-dbc6-4e6f-bbec-fef9d327160f · outbound

This paper cites Journal of the ACM (JACM) , volume=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Journal of the ACM (JACM) , volume=

Reference 31

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:2f06b8514202cf3fd73411ed098c8109a346f332af30309280ff09e65e8bb683

Observation cfbe3845-17fc-434e-8593-91ad168b7ea9 · outbound

This paper cites 2010 IEEE international symposium on information theory , pages=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? 2010 IEEE international symposium on information theory , pages=

Reference 32

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:536b25d4d9ce5eadaaaad538b251b685309612bfdfc489c68f29e7956cb65657

Observation fecc2aab-42e0-4118-adb7-7762066cd8aa · outbound

This paper cites The Annals of Statistics , pages=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? The Annals of Statistics , pages=

Reference 33

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:cd2e9a9337b6dc997ec88b24ce38dd76ae51130d1ee7eb523dc7fd63c31fbb99

Observation 35cce021-12d5-4557-bcf2-44850108431d · outbound

This paper cites Journal of the American Mathematical Society , volume=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Journal of the American Mathematical Society , volume=

Reference 34

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:24d184634e4545b476f666162938e6b8fe5969a5673e46216b012f0deaa9345d

Observation 08224f79-aff8-4194-8655-dfb55b3dc22b · outbound

This paper cites an unresolved cited work.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Unresolved cited work

Reference 35

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no resolver link, observed 2026-07-14T09:13:20.561611Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:3dedb01355f932621b4c32fef9f05ab5bb1473e79b76e7c7fd859076823fab6a

Observation 41c391e4-3b69-4710-859a-4d663e3753a3 · outbound

This paper cites Proceedings of the 20th International Database Engineering & Applications Symposium , year=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Proceedings of the 20th International Database Engineering & Applications Symposium , year=

Reference 36

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:f632e6ec853cd51a39a485af8fd30503c48fc327880dd4c1ddb85c9bd3274821

Observation 4cde505f-f4dd-4c5a-9bcd-f249bae6e009 · outbound

This paper cites Workshop on Multimedia & Security , year=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Workshop on Multimedia & Security , year=

Reference 38

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:b86ada8565245d6d00cc0c0898e8dd9089acace407d2fc572df3014fde97ecf9

Observation 74343220-6812-495b-b5e2-34e4549bd66d · outbound

This paper cites , title =.

Can Watermarking Techniques Help Prevent LLM Model Stealing? , title =

Reference 40

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verified exact
arxiv_id, observed 2026-07-14T09:20:22.290661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:c325492884319b746e23d49c9281d8bb720bbb60107d96e43c379da4243f6d37

Observation 1badcb6e-2452-4625-b1c3-7289a3132288 · outbound

This paper cites an unresolved cited work.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Unresolved cited work

Reference 41

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:db3cfb7a094e915ce5b82feff4a3505c4c28e8c18aba7814028844ea063825e9

Observation 64e11607-5ca7-4adf-9d04-6f5b8d625f27 · outbound

This paper cites Information Hiding , year=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Information Hiding , year=

Reference 42

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:b689aec56d328f969c06c2968ac5d32a991d4fc3069d5940062d177f69e7fc6a

Observation f3ac9b67-a1a8-40af-8b5d-8fb65af8a069 · outbound

This paper cites 2021 IEEE Symposium on Security and Privacy (SP) , pages=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? 2021 IEEE Symposium on Security and Privacy (SP) , pages=

Reference 43

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:197c0b281cf5eedc939aa990d3f3c804ef7abe972f0bc4f5c97f9b968f4de1e2

Observation 03302a92-b7e6-4152-807c-951882d557eb · outbound

This paper cites 33rd USENIX Security Symposium (USENIX Security 24) , pages=.

Can Watermarking Techniques Help Prevent LLM Model Stealing? 33rd USENIX Security Symposium (USENIX Security 24) , pages=

Reference 45

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:dabc7ffa75cc08586f735bc9f2b6450c4f527e62fac21e09c774e36c115a4180

Observation 6d732c08-aed8-498e-99d1-8c302114a725 · outbound

This paper cites Structure and Interpretation of Computer Programs.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Structure and Interpretation of Computer Programs

Reference 46

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:dcc8a166b254b4cd4e56257f1f941e6abd82c50f3ee7b1f86e23648e81b6931a

Observation 133f4b3d-0ed9-4e08-8d4d-83d31cabd55e · outbound

This paper cites Visual Information Extraction with Lixto.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Visual Information Extraction with Lixto

Reference 47

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:b5d551097886f8da3b4da0e1c075ac87b5e87875946d9c0657bb8a07336e0fde

Observation 07cb9ca3-d64a-4280-8848-ef357aebb3bc · outbound

This paper cites Brachman and James G.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Brachman and James G

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:e394eb1587609ba282f82028c5a89ec49eee93ff162cbbe8ce92e639f4a22f45

Observation eab21692-8ae5-4c91-a308-52021095c196 · outbound

This paper cites Complexity results for nonmonotonic logics.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Complexity results for nonmonotonic logics

Reference 49

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:bf96b84b877f424fe636c5b7ad29ede2f3370efd2bf13961deeef1886a2df469

Observation 58bf73d3-d59e-42af-86e5-941975c2f6b4 · outbound

This paper cites Hypertree Decompositions and Tractable Queries.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Hypertree Decompositions and Tractable Queries

Reference 50

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:7b54aca2132b7de3a36fc6c39ac028a795177dd3ce4ace64be50e22b5005395d

Observation 629caf91-80c2-4803-b434-a6998b0e6fdd · outbound

This paper cites Levesque.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Levesque

Reference 51

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:54d551db5fae4272a896aa12685ed263ee3cf400f2d40d95bc0bc4dc3a363b26

Observation 96b9e367-834f-41fa-9e86-96597e8e91e8 · outbound

This paper cites Levesque.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Levesque

Reference 52

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:3f8a2ab78ff4ff698f21f52fe847276c89f34ba3268a83b4dd35dbed2b0c97ea

Observation 6b6d5d62-ef49-4d15-8bba-2b765ed6e7b7 · outbound

This paper cites On the compilability and expressive power of propositional planning formalisms.

Can Watermarking Techniques Help Prevent LLM Model Stealing? On the compilability and expressive power of propositional planning formalisms

Reference 53

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:25b98e2581e86eb4d16386b3894fd4bea1fd3028928a7a7dbdbed3febafc38a1

Observation 1b0a5f9c-99af-477f-b0f6-b68a6a190e21 · outbound

This paper cites Adversarial watermarking transformer: Towards tracing text provenance with data hiding.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Adversarial watermarking transformer: Towards tracing text provenance with data hiding

Reference 54

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:864a930f91ab52bdc402befeb6cc58fd70b8a15605bb0cdaa445f5dab73cb1c2

Observation d00d01c3-344e-4b49-b2d0-e55692faa935 · outbound

This paper cites GPT-4 Technical Report.

Can Watermarking Techniques Help Prevent LLM Model Stealing? GPT-4 Technical Report

Reference 55

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:2a164620a97f2766962ad0b76e783ba43e4e625a253caa999e36f090d4b75efe

Observation 3c0c714a-b6b1-4fd3-86f6-9d6b9852f882 · outbound

This paper cites Claude 3 model family: Opus, sonnet, haiku, 2024.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Claude 3 model family: Opus, sonnet, haiku, 2024

Reference 56

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:a680ac22304fffd32a432d4d977d9392158ac3cdc05c8367e58c3672c14abb93

Observation 73a89bdf-23a0-4f1b-aba5-3cc4b408d5cb · outbound

This paper cites Atallah, Victor Raskin, Michael Crogan, Christian F.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Atallah, Victor Raskin, Michael Crogan, Christian F

Reference 57

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:535bb8e641559b3dcac6045eaf9bf7c14e0e888715c68df9e0f67a07bc952c07

Observation 5837aaf4-a0cc-432a-b1aa-f8c243f64608 · outbound

This paper cites Robust principal component analysis? Journal of the ACM (JACM) , 58(3):1--37, 2011.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Robust principal component analysis? Journal of the ACM (JACM) , 58(3):1--37, 2011

Reference 58

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:e7080442a21c9452723a6235c12c561d0f3e0e2e9b2bf764c771024a5b00beba

Observation ef9fb4c5-45c5-4322-b492-8bf69eaf296f · outbound

This paper cites Cryptanalytic extraction of neural network models.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Cryptanalytic extraction of neural network models

Reference 59

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:405b0f2046e01293c61d2aa812a6e70deaca0e78f4838bef1cb750df50e20973

Observation c592aa7b-14a8-4fcc-af6d-af47ff3d13bb · outbound

This paper cites Polynomial Time Cryptanalytic Extraction of Deep Neural Networks in the Hard-Label Setting.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Polynomial Time Cryptanalytic Extraction of Deep Neural Networks in the Hard-Label Setting

Reference 60

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:06a2cb20978f907d453dbc8127c3f764781c0842f7b5fd7ff6a65a52f868490a

Observation 3c42abf6-f1ef-4a00-b1dc-878cc251cc65 · outbound

This paper cites Stealing Part of a Production Language Model.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Stealing Part of a Production Language Model

Reference 61

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:f5ce2cbf14a42724de975e18db3cb6c797ec4fc28af86d24a394bbb8c959fc1f

Observation 1a2a22e2-b250-47e0-b9a5-8879674938da · outbound

This paper cites The Llama 3 Herd of Models.

Can Watermarking Techniques Help Prevent LLM Model Stealing? The Llama 3 Herd of Models

Reference 62

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:96f9ae06871b94ecb57b47f0b5afe831d87e893acf341a83689b982963e4bb88

Observation d687641a-bb52-45bd-9440-16ac05df958c · outbound

This paper cites Maybank, and Dacheng Tao.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Maybank, and Dacheng Tao

Reference 63

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:0ce15dfa4066504e37e5661cbc80a66cbbf3902ef103936f21dd8e99c1ecd48b

Observation abc6adaa-4282-40c9-8fef-e18a306006e9 · outbound

This paper cites Measuring massive multitask language understanding, 2021.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Measuring massive multitask language understanding, 2021

Reference 64

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:436a2303a2b3953324ebb2bd7f0de8a512a70ec54bd121d1d059829bb57297a6

Observation a63f7892-f6d2-4b63-b1a7-786e90123acd · outbound

This paper cites High accuracy and high fidelity extraction of neural networks.

Can Watermarking Techniques Help Prevent LLM Model Stealing? High accuracy and high fidelity extraction of neural networks

Reference 65

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:d14a42fefe2b39b4234307b052f6c1bd0984f107de2500cee48f8e2ad39a35bd

Observation f204d5c6-eb55-4271-b8a5-67cf98478f02 · outbound

This paper cites A watermark for large language models.

Can Watermarking Techniques Help Prevent LLM Model Stealing? A watermark for large language models

Reference 66

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:c6879a3769473de0d2178d7311c8fa6ba94f54e2d4eb09305ae7738695ded3fa

Observation ad2deb98-b2bc-43be-abaa-ddb46904ec42 · outbound

This paper cites Statistical analysis of effective singular values in matrix rank determination.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Statistical analysis of effective singular values in matrix rank determination

Reference 67

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:7d77419cf4a145522565637296049af3ec1a870024d34fc53e87e5982c4b0e6d

Observation f5634625-0c6f-4d12-9f95-d43a80ee1172 · outbound

This paper cites Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMs.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Waterfall: Framework for Robust and Scalable Text Watermarking and Provenance for LLMs

Reference 68

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:c8ee68fa2f6409808ae67d2cef62a97b4acc0a73e790b764b1917bdf18097f41

Observation ef83bd5e-376f-4041-a7b1-37ac11a37262 · outbound

This paper cites u lent Sankur, A. Sumru \.

Can Watermarking Techniques Help Prevent LLM Model Stealing? u lent Sankur, A. Sumru \

Reference 69

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:5f57bc401e94ccda6320edc0f5ca2f35596b2e47fa408a2b5f2834aeb7536f96

Observation aef8e46c-4348-4264-b0ae-776931aa624b · outbound

This paper cites Model reconstruction from model explanations.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Model reconstruction from model explanations

Reference 70

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:a41dd00d8110e7bdd34f1b94e6538a08cd6a92878557aab29b6580be230b53ca

Observation f24e6783-5e03-4c3b-b419-b8901de594a2 · outbound

This paper cites Language Model Inversion.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Language Model Inversion

Reference 71

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:ef1fcca401f9d7c1b71b0292305e555123038b3bee73d2ed548c44a27a53dc67

Observation 8f8c5357-dc16-48a1-841c-94c58cbdf81f · outbound

This paper cites DeepTextMark: A Deep Learning-Driven Text Watermarking Approach for Identifying Large Language Model Generated Text.

Can Watermarking Techniques Help Prevent LLM Model Stealing? DeepTextMark: A Deep Learning-Driven Text Watermarking Approach for Identifying Large Language Model Generated Text

Reference 72

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:e6a6d87d33a5742e5fd2aa164cfa52c4052cb2a7d642010502c4b46a815612c9

Observation 940267f0-0085-4708-851d-1d93c3e69aa9 · outbound

This paper cites BLAKE3 : A highly parallel cryptographic hash function, 2020.

Can Watermarking Techniques Help Prevent LLM Model Stealing? BLAKE3 : A highly parallel cryptographic hash function, 2020

Reference 73

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:9b22643dc7c83405ebd5a881ab221b5edd32270925258cbd85e8a3d4564cd313

Observation 7694e71f-2275-4e61-8c54-99aa4ab57ea6 · outbound

This paper cites an unresolved cited work.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Unresolved cited work

Reference 74

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:3215d01957da56770df7c6d7d65f01e792188796c50f5debbf18d974e5e36897

Observation 32a4b5b9-0027-4720-85ad-d22fa44f247c · outbound

This paper cites Unispach: A text-based data hiding method using unicode space characters.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Unispach: A text-based data hiding method using unicode space characters

Reference 75

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:2517eb5b304eebaef259851e85098249d88ae75510e962b6a69a0da39d38b18d

Observation c6a843eb-b0d6-406d-8e33-3d29e69db2c4 · outbound

This paper cites an unresolved cited work.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Unresolved cited work

Reference 76

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:d203a727600d4da3ec55fa25514b6c35facebd65c5ef6dd2b7dce518e35b0d96

Observation a1934ba0-bf18-4772-b9f3-1aaee8079f76 · outbound

This paper cites Microsoft probing if deepseek-linked group improperly obtained openai data.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Microsoft probing if deepseek-linked group improperly obtained openai data

Reference 77

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:7f4f7e2acba41ca74bcf48a32bdffa3b483e6071746e7a1cc828518840de35c3

Observation 4ae5edd3-41e8-4775-8445-a0e3be2bb2cd · outbound

This paper cites Content-preserving text watermarking through unicode homoglyph substitution.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Content-preserving text watermarking through unicode homoglyph substitution

Reference 78

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:29efae5bd2c406047b74b808cab12b8abd189a9fecf5deb4c5111bdc175bc313

Observation be598b8f-fdef-4e69-996e-6a372d231f1a · outbound

This paper cites Reverse-engineering deep relu networks.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Reverse-engineering deep relu networks

Reference 79

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:04f293cb8a8a0b43d3a2acb6be117eadb5e6b0ed008b26ae6b3d90f02cc8d358

Observation 9408fab9-d14d-48e6-85b1-70ab76f4b0d3 · outbound

This paper cites Perturbation theory for the singular value decomposition.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Perturbation theory for the singular value decomposition

Reference 80

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:22f8ad7677a0b8bbad4e021d305dbf73bd05b5bb29bb7c7fe772d6ca0bffe106

Observation b3c33400-ff0a-4c98-8bd4-cefdffb1012d · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Gemini: A Family of Highly Capable Multimodal Models

Reference 81

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:d9c1003cbf39d120e4e110dbc1ddea978bed381f4b6762ebf4022cd8b39d95ba

Observation 989aa7fb-bfc4-4ecd-9b27-4b510d2b18a2 · outbound

This paper cites an unresolved cited work.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Unresolved cited work

Reference 82

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:8c284b397627751b0534c0630003f23a807921adea44045fa5d08fa1d58c1a89

Observation fbabd3cd-e8bd-414e-a836-a53c7d71181e · outbound

This paper cites an unresolved cited work.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Unresolved cited work

Reference 83

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:99608cd3c513abf38b27ad4f2509971e48341371136f780d5c2b26048f3bfc50

Observation 2c01bafc-8b74-424d-8a54-7b624bc58497 · outbound

This paper cites Reiter, and Thomas Ristenpart.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Reiter, and Thomas Ristenpart

Reference 84

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:955f9d5ac5ea4c8bcbe87a5c52e39106c9ae198b0b1acdae562825fc0509a42e

Observation 248d8216-3eac-4328-8306-9df7fb79fefa · outbound

This paper cites High-dimensional probability: An introduction with applications in data science , volume 47.

Can Watermarking Techniques Help Prevent LLM Model Stealing? High-dimensional probability: An introduction with applications in data science , volume 47

Reference 85

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:8e1cf6d707cf452b9aebf30f2d60a60982d8e64dadca6bd6d82bc523ac7d6818

Observation 8554e566-ffce-4d0d-b88a-09b9043340f4 · outbound

This paper cites Das asymptotische verteilungsgesetz der eigenwerte linearer partieller differentialgleichungen (mit einer anwendung auf die theorie der hohlraumstrahlung).

Can Watermarking Techniques Help Prevent LLM Model Stealing? Das asymptotische verteilungsgesetz der eigenwerte linearer partieller differentialgleichungen (mit einer anwendung auf die theorie der hohlraumstrahlung)

Reference 86

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:e6e06bde89b3fc3f6eb56c97bbc01479cd7b391b863255d43f7c02a20017981b

Observation 90fc797e-7a2e-4b32-b334-a2c09a41e221 · outbound

This paper cites A survey on knowledge distillation of large language models, 2024.

Can Watermarking Techniques Help Prevent LLM Model Stealing? A survey on knowledge distillation of large language models, 2024

Reference 87

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source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:60123653a2cdd31de47e74e641e2143d795fd015064c6e1ee907f97af9c18693

Observation 30c78ea2-b86b-4ec0-b3e9-f944691ba67f · outbound

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

Can Watermarking Techniques Help Prevent LLM Model Stealing? Watermarking Text Generated by Black-Box Language Models

Reference 88

Resolution
unresolved
no resolver link, observed 2026-07-14T09:13:20.561611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:d8e086fa9aa6f058cb3a1ecf7f172b5c8325698d3c45441ef7782ed212a0679e

Observation d50c5065-4313-4c8d-be07-d1a719bda8e3 · outbound

This paper cites \ REMARK-LLM \ : A robust and efficient watermarking framework for generative large language models.

Can Watermarking Techniques Help Prevent LLM Model Stealing? \ REMARK-LLM \ : A robust and efficient watermarking framework for generative large language models

Reference 89

Resolution
unresolved
no resolver link, observed 2026-07-14T09:13:20.561611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:0a5095c15ae7b127445bb516441fcbfc6120c16d4ecc52207d2cb4068bdd954b

Observation 9b6d2cfc-4d52-4ef7-baef-eb252a22c415 · outbound

This paper cites Protecting language generation models via invisible watermarking.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Protecting language generation models via invisible watermarking

Reference 90

Resolution
unresolved
no resolver link, observed 2026-07-14T09:13:20.561611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:da5eb34706528b21d65b69de2c0e0bfa7073503b16a8de29b572a87c78c9b847

Observation 43880f62-9744-42f0-b81e-87ea27f6a84a · outbound

This paper cites Stable principal component pursuit.

Can Watermarking Techniques Help Prevent LLM Model Stealing? Stable principal component pursuit

Reference 91

Resolution
unresolved
no resolver link, observed 2026-07-14T09:13:20.561611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T09:13:20.561611Z digest=sha256:d2b598275e3df718f3bdf6542d343d7e85bb451b2091ea03004fc8a8d8eb668c

Pith citing papers

No inbound Pith citation observations are available.