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Paper Citation Record · LEDGER

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

As of 11 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations 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 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

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=sha256:11c22cd91b08bb63589fc174567a0fb0b76dff9de6c4aa2f07e468c7c1597a40

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.721069Z digest=sha256:36fa9549d01908ac5ae36df5af83c1c69b259b4f0268a9af77d06c51d5dc7939

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.725633Z digest=sha256:cf3ecb43fe698caa1cc00efea289dcf09b39f7c1ced0c37e19b393ff61e26b67

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.730555Z digest=sha256:c4630a9767a2eb967b62ff459ac7190bc1f31e630ca381d7fff6a40777bd3781

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.735560Z digest=sha256:ba2e58dde07fa8ff23db0ffc8a15ad3f60fc7f4783f5bc711dea6aff7b73f82d

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=sha256:83fd34d9f7826e4d93000be49b590c0d04448ea73d1f73c7020111ff9c10c1ba

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:d7ddc19c03c4fede516703f70800297c983a3dffe40ea071ca22add66d72b6ff

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=sha256:09cacb2eb351f7ea9d54f85d228f2084d6d9c0f71f85a9c5275592264891419b

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:b60e809c8aa79a293fa5e22aefcb557381a41dc35f76d8a9bf52fccb8d3846bc

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.767222Z digest=sha256:c0b1f430632f8f6322ddf4e8dd58f560cd20559ba8fa828df81591601dbcfaa1

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.771980Z digest=sha256:0c89dd001fddc4cc98b0fe489062f863b85e9555cfe8b02ec04afdc0a9d35474

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:f5ef92f02b2ff3b0bd06a9771f918f7d4044f8106e2329d669a8dadcc95c13e9

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=sha256:3d593e9cd2d4e19c3f4979bd4cd13985f77e581f36cede28400d99ba5b54fd0f

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=sha256:5555e87d17ce229aa9dfb9c794324a809e6df8093ad36552462d85ff0cda05f8

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.792371Z digest=sha256:60cbcfb302aaead7012727fc84d1be32d88ec2d237d6e9d175b344b35d1648bc

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=sha256:48cc339faa5fcf46cd69854c644e5ae9a971ddf9dc3ac9e7a3450053907397d9

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=sha256:910383338e49ccb00838e6154229a6062c9e1d4eba9b6ba892c84f6acff228cc

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=sha256:1bc6eadbd2e992e1f7828dc8b1cf9cbc6005ceb7c2c485ec98cc839aedfd5cd8

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.812578Z digest=sha256:6edc84b70735f5756433b1f45067208e6a3ac9c7fddd0ee87f676ac2a2d40737

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:b45c0727c71e22b9d1ac6bb72977176683b8ab87cbd6ee17684625503e7651bc

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:c23463c6e17faa69f77b01f0198ea9643244e223a9689ce4805f688ba8727a64

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.826539Z digest=sha256:95ce4dd3aff2344f2f662c34f2976bb504dc4a4cc3b2e458fe553c09cee5eab6

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.831403Z digest=sha256:2d1b3bbd762949eff3b9855c929d0dbb686bbf883007d2fec2f4de9c1bf175cd

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:f70eb07036f831ff5359df7243093ae7b587ada66b02db66d167e34367f9eb2f

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.841033Z digest=sha256:a4d14e0cb0aedbdbcbe17989fba68a066e0c222b1dadc7150d0149149407b9f8

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=sha256:86680fcf32ab04ef32aea33ddf4c84b558a15653a043d6124010731c5cbdcb15

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.851066Z digest=sha256:ab24fef5bfb9b845d2ae07341e3b4a785b90ff0dfec8e5fbd4a12d649998f2e8

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=sha256:63d6a502f3ce89964fc1c4f0a09d86a9c3903e9b678545b0411e433d2e92b3ca

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=sha256:896c2870227233b18d88910866aee4a53805477af19477296e0efeeb2aba87ca

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:d22db66db5536ad02a4360baef3f6465c67bdc4ddbc475072f77e9d19bb2c13a

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=sha256:2f67f942285a088d98417e48ccf28100aedbd8808acffd727082e9e5019424d7

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.875639Z digest=sha256:6c4c4374255aee5be5f702b0f67a85851dfd9b8073350c366b4d397eba715a2b

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.880749Z digest=sha256:a3a9d134523479078f3d21f5d75f74e5c63c70e36be89decd3689060dccbd752

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=sha256:2e1594110e0295eebabc37c1fcd0cf4f9b3283d8e1ecf5ce51a1e4b1482c1a02

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=sha256:860865fe0ef5016512342eadcc325c5abfe6f11ff61e51569d78d70c1cd969ba

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=sha256:60806f715ec032657ab4bbfadebe87f074296861362663a849b14fec6ea2e902

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.901388Z digest=sha256:2bcf9a68fd6c2d1e98eab5385b70139544eea302a68270449692631f27a4d91d

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.906889Z digest=sha256:587f20341cf002260a433dc64cbdcdf0ba2fbfd5bc4427c47dcaa1187f9bd384

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 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-10T17:36:52.911992Z digest=sha256:03c251a870ce60bf765873243bd11e6744ed7e6d794564166ccebb4173010754

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=sha256:4e2f4c42c1db5c7a73304681159aed27decf015e6d0b6db2ee7b7065f3e67698

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=sha256:56152c7498fca4b383fb3270172f3b04457fc1c73cdff849f8676e4f5ebc5dd1

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:b2254c8bef6cbf7c2d65cc43c61c6b2e338d4f2f5c94f134f6f42fcccc4a6c33

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=sha256:4f904266e98fa2614cac0f7bfc250b013d9a6d7418b6b279087f5827de9518bc

Pith citing papers

No inbound Pith citation observations are available.