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Paper Citation Record · LEDGER

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks

As of 20 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2501.12174.

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

pith.paper-citation-record.v1
2501.12174 v6

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:36:52.931279Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:45:38.613380Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-16T10:45:38.961666Z

Reference resolution

43 of 43 outbound references displayed

  • verified exact1
  • verified fuzzy15
  • unresolved27
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 88a5c7d1-2d5b-4cb3-9a4d-55421621a3b5 · outbound

This paper cites Program Synthesis with Large Language Models.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Program Synthesis with Large Language Models

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.715494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.715494Z digest=sha256:57664342de42f67418fb6d64d69f69a788465943ce5c265d2c74615899da3ded

Observation 3327a4d8-17c3-4667-a982-ed5dd9532cfc · outbound

This paper cites M., Gebru, T., McMillan-Major, A., and Shmitchell, S.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks M., Gebru, T., McMillan-Major, A., and Shmitchell, S

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:36:53.625479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.721069Z digest=sha256:e362de877799e94de45ff752c8474085416e14e28fa508141b57c90fa7846a3e

Observation ea952623-6d84-4046-9462-e2f7dad15b74 · outbound

This paper cites S., Abercrombie, G., Spruit, S., Hovy, D., Dinan, E., Boureau, Y.-L., and Rieser, V.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks S., Abercrombie, G., Spruit, S., Hovy, D., Dinan, E., Boureau, Y.-L., and Rieser, V

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:36:53.608104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.725633Z digest=sha256:7822bae6631938c52d45d90906b9e60008095cb5a05651d1dcf9a9580d2a57cd

Observation 72fa5c9b-8e26-461a-b0a2-69c165d1a929 · outbound

This paper cites Bad characters: Imperceptible nlp attacks.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Bad characters: Imperceptible nlp attacks

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:36:53.588473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.730555Z digest=sha256:4bb0ecb5e4fb25d0fb97f0a52fcaba5eccf5971eea29db9f4aa49afb9611302f

Observation c98aa731-547f-4fb8-9ae5-440b1ef33379 · outbound

This paper cites T., Low, S., Maxemchuk, N.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks T., Low, S., Maxemchuk, N

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:36:53.570718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.735560Z digest=sha256:5b59239a058b1f44d245135341735e00cbfe6b2f8d39cee1d30ba7a7c7b21a6b

Observation f557fce2-9b8a-4b6f-a10e-b5526c280b4b · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.740666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.740666Z digest=sha256:bfd33e3a86992f11be8da9bd61e0fb55c9ad6e3f21d5fd77f50ed3d64745a353

Observation 35af2352-96b5-4394-a96a-0174ea7d5196 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Evaluating Large Language Models Trained on Code

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.746363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.746363Z digest=sha256:c05db0650b547854fb6992121190cfa212af7117b3bb4acaa660575730e9c685

Observation a8832e4c-34cd-4342-8f27-f3c66f12a80d · outbound

This paper cites Undetectable Watermarks for Language Models.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Undetectable Watermarks for Language Models

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.756227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.756227Z digest=sha256:8fb68c8d60f17e58c4bd4060620817962cb067e4a77d734cae579f69d664f382

Observation 4413a64b-0ac7-4116-b9af-8c31cf4aea9d · outbound

This paper cites Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.760844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.760844Z digest=sha256:a52556d1ee79738fe2d6571e705ed411453b9a83ba3847c02f2ad17488c70b91

Observation 0405cdcc-c2c8-452f-82bc-7f552234578c · outbound

This paper cites WordNet: An electronic lexical database.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks WordNet: An electronic lexical database

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:36:53.553956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.767222Z digest=sha256:604934a6a6d5d9efc1232ef98fa37451e890cb733a999566f651a2e5bb4237da

Observation fffd1fcc-c579-41d4-aeea-d224589e16f0 · outbound

This paper cites Three bricks to consolidate watermarks for large language models.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Three bricks to consolidate watermarks for large language models

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:36:53.537949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.771980Z digest=sha256:23b0ce5a135940ae7fdebd613882216ea20cf7c1ede846b76616a307534b03f0

Observation bd3e8999-114e-41bc-a967-af1415adef2f · outbound

This paper cites The Ethical Need for Watermarks in Machine-Generated Language.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks The Ethical Need for Watermarks in Machine-Generated Language

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.776594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.776594Z digest=sha256:29708429c80e2a9f62ab2d1380f62edcb99664d477da978496998f773f4e457c

Observation 61d4b8e9-76a0-4a23-bffd-c513cf4ea5ba · outbound

This paper cites The Curious Case of Neural Text Degeneration.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks The Curious Case of Neural Text Degeneration

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.781954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.781954Z digest=sha256:04c41da64dea647f6db135f3911caf296d4187c4e49e77797fe76fd07668ec89

Observation eaabdaaf-59eb-4bd3-844a-773cd9cf2365 · outbound

This paper cites Automatic Detection of Machine Generated Text: A Critical Survey.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Automatic Detection of Machine Generated Text: A Critical Survey

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.787164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.787164Z digest=sha256:280e43360d35e44ea9ba802edd8c7c7259654fd84d660c20c696ea5625d70d06

Observation 403d3c35-bab7-4d0d-a375-63470f49f460 · outbound

This paper cites A watermark for large language models.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks A watermark for large language models

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:36:53.521812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.792371Z digest=sha256:deb4914d0e353042ec2409fa0ae0f445d6e9d17aa46fc52acc71e643d8ce4df9

Observation 88a54f31-d1c7-4e59-95c0-eb7032c7b6cc · outbound

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

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks On the Reliability of Watermarks for Large Language Models

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.797096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.797096Z digest=sha256:aeca4f83686cdd66a7212ff1aded7fdc2d08476f893f3c48bbd139d4de4cb1f3

Observation 46e8e29a-c101-41cf-9606-05d2afb91ff2 · outbound

This paper cites Robust Distortion-free Watermarks for Language Models.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Robust Distortion-free Watermarks for Language Models

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.802135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.802135Z digest=sha256:7e30679d36e81721ddf35a445e1eb9e16dd6266e0a93ebfe6c7084200a5bfa6d

Observation a398238b-dc88-4c25-93f4-40f641967e1a · outbound

This paper cites Crafting papers on machine learning.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Crafting papers on machine learning

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.807400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.807400Z digest=sha256:7f2f8f9db108a085010b04612bdd213645165c42b8d353148d6119fc10395899

Observation df646cb4-438d-42ce-befa-73924e5fd677 · outbound

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

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Who wrote this code? watermarking for code generation

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:36:53.494121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.812578Z digest=sha256:0d1d6cd8ea89a74e53524bc2ff0a976ebfd0c380cadc7057b81c39550cdbbffe

Observation 9a402488-3288-4ff3-99de-a4f1edef5d56 · outbound

This paper cites StarCoder: may the source be with you!.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks StarCoder: may the source be with you!

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.817306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.817306Z digest=sha256:7169b84e35b39ade571df2d0ee77d02a77cfe2d661b7f2783a8dec236b4750c7

Observation 1193cf62-140f-4926-82eb-4697c1eb5f27 · outbound

This paper cites A semantic invariant robust watermark for large language models.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks A semantic invariant robust watermark for large language models

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.822257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.822257Z digest=sha256:c4d3518b6f9d72389a2413106e5a994d6da11fad5c3f32f34a7ed398a70b7040

Observation 1994e129-3b36-4ea4-86bd-27471abcb50c · outbound

This paper cites A survey of text watermarking in the era of large language models.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks A survey of text watermarking in the era of large language models

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:36:53.466753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.826539Z digest=sha256:e1d376a5bd5400f57e8a11f713eed5493a96c212e3e2bab429423f5277ddf08e

Observation 7eb77a27-00af-4884-a170-b5e40199c7a0 · outbound

This paper cites An entropy-based text watermarking detection method.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks An entropy-based text watermarking detection method

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:36:53.449159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.831403Z digest=sha256:fc99673dd89940bee1bc9c53cbd9ff3cbc1512ef2d4af78b2c51107327855e75

Observation df5643bf-a7d8-4390-9879-6ca3b339deb4 · outbound

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

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks DeepTextMark: A Deep Learning-Driven Text Watermarking Approach for Identifying Large Language Model Generated Text

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.835976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.835976Z digest=sha256:fdf1e0ddf38e11438f0c89a9c85ab6200d017fc9079d0ca0e017adf07deb767d

Observation 7757b419-2884-4d8c-a2b3-13eafa550433 · outbound

This paper cites Chatgpt: Optimizing language models for dialogue.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Chatgpt: Optimizing language models for dialogue

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:36:53.431490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.841033Z digest=sha256:672fb482075b78f88fcb1db19ffd56e36ded6f1d4fe10eaa5e2184774b54634e

Observation 047e7bab-949e-46e2-b718-0550e67e4eef · outbound

This paper cites an unresolved cited work.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Unresolved cited work

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.845917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.845917Z digest=sha256:cf2e4081bc92f5cc9e45291af24cc6bacbd7e574148a5083a696f4f8e1d28aa3

Observation bcaf5465-0313-4827-b282-66d827bf4511 · outbound

This paper cites WaterSeeker: Pioneering Efficient Detection of Watermarked Segments in Large Documents.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks WaterSeeker: Pioneering Efficient Detection of Watermarked Segments in Large Documents

Reference 28

Resolution
verified exact
local_arxiv, observed 2026-08-10T17:36:53.130834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.851066Z digest=sha256:8bd7bd46fd6a869e106db7e87e3f4124a1517e0a1c70c45ddedca52f2a97220c

Observation 8537c121-ed62-4475-a85f-b9fb1521b327 · outbound

This paper cites Y., Wong, K., and Chee, K.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Y., Wong, K., and Chee, K

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.856097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.856097Z digest=sha256:3823b97dc58c26afa88f2a53b660f054130b71b78ecf7399a2b0a79f6a39f017

Observation ec7fcd07-9cfb-4419-906d-b40c1354fbf5 · outbound

This paper cites W., Xu, T., Brockman, G., McLeavey, C., and Sutskever, I.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks W., Xu, T., Brockman, G., McLeavey, C., and Sutskever, I

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.861263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.861263Z digest=sha256:cb71c8dbd66ecde01cb8dc61e6975c64d2333bdc45e357e2369849a529c059e0

Observation 11ee5fb9-4f1e-4424-a215-209598cd3b93 · outbound

This paper cites an unresolved cited work.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Unresolved cited work

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.866072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.866072Z digest=sha256:dc4465e9e1bda39cf784742e85dcedadf4edcbe01cdc1489573a0e266348bd8e

Observation f2d5cbfd-2e9e-46ae-a6f6-479d04574547 · outbound

This paper cites A robust semantics-based watermark for large language model against paraphrasing.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks A robust semantics-based watermark for large language model against paraphrasing

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.870963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.870963Z digest=sha256:cb68025ae0727a588e13979d83e77c484406dad44df08c20d46c26d5f58895cf

Observation 62496673-7238-4bcf-96c2-c1c7eb902fff · outbound

This paper cites Embarrassingly simple text watermarks.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Embarrassingly simple text watermarks

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:36:53.393127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.875639Z digest=sha256:7a17d7fc86dd2b509ecb87f45d32fc6ad57d3f8292ee70fd8bef1942aa6697af

Observation 6e271043-3d29-4cc1-9d17-ad17af4cff66 · outbound

This paper cites an unresolved cited work.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Unresolved cited work

Reference 34

Resolution
unresolved
raw_fallback, observed 2026-08-10T17:36:53.377229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.880749Z digest=sha256:5fa15914c7153e75c7f2a6bbad4c23292d48d5e1c34510adb178ca437e25f420

Observation b300c886-2145-498b-b154-3785a4fa5f10 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks LLaMA: Open and Efficient Foundation Language Models

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.885908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.885908Z digest=sha256:c1670da54481b51b246a89f542c38fa718f7872f6fec8e2b5b55974156c63859

Observation 7e42d69d-d9fe-406e-88b9-d80ef08986da · outbound

This paper cites WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.891073Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.891073Z digest=sha256:f5b2cc98e697e0ef4f3a6f53cba58260734efa00e2a2d183d5182a98c709cf53

Observation 77524688-d73c-4947-a64b-b15812196d33 · outbound

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

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Towards Codable Watermarking for Injecting Multi-bits Information to LLMs

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.896133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.896133Z digest=sha256:fb55ba1fab746d56b109a8251086435464b023fcf608b3a8c57781104d15fcaa

Observation 02d0280c-5198-456f-ac44-997faadd2f92 · outbound

This paper cites Fairness feedback loops: training on synthetic data amplifies bias.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Fairness feedback loops: training on synthetic data amplifies bias

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:36:53.361685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.901388Z digest=sha256:ee326067511753ed4592927e8871e684bfece852dc7e20d7ec0a2caa46e3db9c

Observation 3bb748a8-a20e-4000-afa3-7c65cb031d17 · outbound

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

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Tracing text provenance via context-aware lexical substitution

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:36:53.345288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.906889Z digest=sha256:a80f76920b89403d47b3bfabe6fdda957ac8b52a7d2642a8b583e2750cc934d2

Observation 6844589f-a293-4f45-9564-6b539401f06d · outbound

This paper cites Watermarking text generated by black-box language models.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Watermarking text generated by black-box language models

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T17:36:53.328873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.911992Z digest=sha256:64e8b5956c7498166c2790496f1553f1a4330ee481a1d077347b8a0cf940e089

Observation 56206cb5-ba81-4ec2-9fdf-441854a6f53e · outbound

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

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Advancing beyond identification: Multi-bit watermark for large language models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.916755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.916755Z digest=sha256:8e9d2552770e0939158eef79bea38d5a5f0147f2459f45894ee6beacfebbfe1b

Observation 8189dde8-e2f8-493e-aef7-2f1228e7df4e · outbound

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

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks OPT: Open Pre-trained Transformer Language Models

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.921613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.921613Z digest=sha256:9076eb864452ddf573a15ae371ca15354787a7a35940c9c4f3b0bf5665ccb354

Observation ef4acb33-d650-4224-85f2-74cff894a55e · outbound

This paper cites Provable Robust Watermarking for AI-Generated Text.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks Provable Robust Watermarking for AI-Generated Text

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.926617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.926617Z digest=sha256:4f189f2fa93f68f5d0be96f4d98d241de9cb5149c58c5cc54de42434a7cd643d

Observation 12e52c7b-0797-48c8-88df-6b6680db8e86 · outbound

This paper cites write newline.

BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks write newline

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-10T17:36:52.931279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T17:36:52.931279Z digest=sha256:c124bacecf59dd9391949ad3944b81f920a91e576b34812de7af71541246a33c

Pith citing papers

Observation a9d464af-b5b7-4296-91e1-90ab8a429f4c · inbound

Unified attacks to large language model watermarks: spoofing and scrubbing in unauthorized knowledge distillation cites this paper.

Unified attacks to large language model watermarks: spoofing and scrubbing in unauthorized knowledge distillation BiMarker: Enhancing Text Watermark Detection for Large Language Models with Bipolar Watermarks

Reference 28

Resolution
verified exact
local_arxiv, observed 2026-08-16T10:45:38.965430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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