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Paper Citation Record · LEDGER

A Dual-Hypothesis Reasoning Framework for LLM Guardrails

As of 7 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2607.17575.

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

pith.paper-citation-record.v1
2607.17575 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T17:41:14.893434Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

39 of 39 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved37
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a676ecb6-680c-4c2c-ba6d-b23ceab34b1d · outbound

This paper cites 2025 , eprint=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails 2025 , eprint=

Reference 1

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source=arxiv_source observed=2026-08-01T17:41:09.906501Z digest=sha256:10b2e9502664a43f334abe53c969edb1fc1eaf6ce148ac5045eba319094e149f

Observation 699330c4-26f0-44ab-96a3-24dddb3cd541 · outbound

This paper cites , journal=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails , journal=

Reference 2

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source=arxiv_source observed=2026-08-01T17:41:10.004070Z digest=sha256:2022578654dc49325136b047d4f0a81d92f70cc8072cdfaab0e4004d71f422d5

Observation 0e944141-cdd2-4148-a6cc-1d3a91e2fa8c · outbound

This paper cites Focal Loss for Dense Object Detection , year=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails Focal Loss for Dense Object Detection , year=

Reference 3

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source=arxiv_source observed=2026-08-01T17:41:10.132874Z digest=sha256:18ec84102481880656ff38fe5152041737bfc31644d8da5f8b288df4734709ae

Observation 729ce0d3-1b6d-40bd-b03f-6e73b58bb24c · outbound

This paper cites Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , month =.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , month =

Reference 4

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source=arxiv_source observed=2026-08-01T17:41:10.188117Z digest=sha256:4ce94c5a1ebf7b55e133ab40492fedf03164a9e59a23167bdb1102ba6d8f44f9

Observation 9fb6e5dd-6ebe-4a37-be9f-c1dde874aac1 · outbound

This paper cites Advances in neural information processing systems , volume=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails Advances in neural information processing systems , volume=

Reference 5

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source=arxiv_source observed=2026-08-01T17:41:10.310374Z digest=sha256:298a9823d6a466d1756bd12557afef19efdb2dccb642775ed69d226dfac23948

Observation a2868f8e-a838-41dc-b94c-b5e5ed5df194 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails Advances in Neural Information Processing Systems , volume=

Reference 6

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source=arxiv_source observed=2026-08-01T17:41:10.476439Z digest=sha256:f98807dfde414f31c4bf4bb551806fbca8a052b79de10b84e0b6674baa707fb7

Observation 21d5c186-1164-442d-9898-ed1c67077bcc · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails Advances in Neural Information Processing Systems , volume=

Reference 7

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source=arxiv_source observed=2026-08-01T17:41:10.646485Z digest=sha256:6d79d8d31bab41edecf8c8facb7d62baf579adbae981f773aff1c4cd6b4664cb

Observation e0ed4f7c-85ae-473a-a25e-8456e162fcf1 · outbound

This paper cites International Conference on Learning Representations , year=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails International Conference on Learning Representations , year=

Reference 8

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source=arxiv_source observed=2026-08-01T17:41:10.794742Z digest=sha256:94815a675ff60cf8febd1833d8b3c7fdaf3e402071e33663714a322c52de7c58

Observation 924c4245-b0bd-4517-b052-35debd7160aa · outbound

This paper cites and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu , booktitle=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu , booktitle=

Reference 9

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source=arxiv_source observed=2026-08-01T17:41:10.980147Z digest=sha256:7347f14bb7a64193cdc6ec5876b2aae9262530f417f5dab395ce65e7f00443bb

Observation cc17a8c6-4803-4e58-a64d-af53bdb010ed · outbound

This paper cites 2024 , eprint=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails 2024 , eprint=

Reference 10

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source=arxiv_source observed=2026-08-01T17:41:11.137628Z digest=sha256:0725cb25f0e26498b2f43df23c7bfc8bc1ef42dd3ca4a882b23925289b1c6656

Observation 676a8799-868f-4ea3-b635-9309624deefb · outbound

This paper cites 2023 , eprint=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails 2023 , eprint=

Reference 11

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source=arxiv_source observed=2026-08-01T17:41:11.275474Z digest=sha256:bc80d0a663548cea13ff00c3c0521c230c8fa16499357a597bbf3edc2ca6e3f7

Observation b4ff3818-7ec1-4014-badf-d67bcb19da28 · outbound

This paper cites AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Experts.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Experts

Reference 12

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source=arxiv_source observed=2026-08-01T17:41:11.392807Z digest=sha256:3154c6e25f4f0e02d2bb99366c0534a77a9c94deb517f3044a98bdfc4a12c50e

Observation 75a9ad8d-38e8-4f95-b3bf-e0e8254f3d51 · outbound

This paper cites Why Should I Trust You?.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails Why Should I Trust You?

Reference 13

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source=arxiv_source observed=2026-08-01T17:41:11.552506Z digest=sha256:595042e6ed6a65e377694010aeb365fe194ba40cbe0de0387c36d9c030bc5c83

Observation d64b639d-4de7-4621-ad44-aca8c8657b96 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails Advances in Neural Information Processing Systems , volume=

Reference 14

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source=arxiv_source observed=2026-08-01T17:41:11.742087Z digest=sha256:8c84a67e8d8c1c00397ea97bc5d76cb9ba00b20dcd794f30c917809e851b4dd3

Observation 1bc1f87d-385a-4433-91f2-33b429b9c815 · outbound

This paper cites Safety Layers in Aligned Large Language Models: The Key to LLM Security.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails Safety Layers in Aligned Large Language Models: The Key to LLM Security

Reference 15

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source=arxiv_source observed=2026-08-01T17:41:11.909026Z digest=sha256:81d19d19f13b406c2e847a9e824fe764603c0028fdd935b61c006fc413333644

Observation 20830b46-1ab2-448e-95b6-2edad9f92057 · outbound

This paper cites 2024 , eprint=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails 2024 , eprint=

Reference 16

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source=arxiv_source observed=2026-08-01T17:41:12.052868Z digest=sha256:21960d1a3205bfcf40d0cc4d4614199eb23b32bad84fe54db8975c3db40a61af

Observation 7c08dfc8-3af8-42a7-9475-f64cb4a8dc78 · outbound

This paper cites 2025 , eprint=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails 2025 , eprint=

Reference 17

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source=arxiv_source observed=2026-08-01T17:41:12.214850Z digest=sha256:576130affb277bd17acc8ab29a47ea0deb6b304932975bf07a75e8b84e6d784a

Observation 5d3f6084-5e55-4597-a282-eade13692f77 · outbound

This paper cites Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security , pages =.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security , pages =

Reference 18

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source=arxiv_source observed=2026-08-01T17:41:12.379348Z digest=sha256:1ae5f0ba680f50eeb3e5cfe1f6ecbe73a264408373cc0801b325416669bce4b6

Observation 946b17c7-1c3c-47ad-bcb9-732a306d42ee · outbound

This paper cites 2024 , eprint=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails 2024 , eprint=

Reference 19

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source=arxiv_source observed=2026-08-01T17:41:12.585065Z digest=sha256:fb4decf3e4d83287a41a341c876e03c3666ecf4ed5bdde5872a16086ae1c9773

Observation 5c49e62c-a0fa-4554-99bb-9e5d8172e279 · outbound

This paper cites 2025 , eprint=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails 2025 , eprint=

Reference 20

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source=arxiv_source observed=2026-08-01T17:41:12.742454Z digest=sha256:254e4858b1d6e287b734bd8b6246f52555adb5180e0dfd285aa6f71b26391289

Observation 8594fac1-4aba-4d78-b4ff-1d2dcf19034d · outbound

This paper cites 2025 , eprint=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails 2025 , eprint=

Reference 21

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source=arxiv_source observed=2026-08-01T17:41:12.807426Z digest=sha256:9931227668faf7cf5da158f45ba3f66d879146176a09fcb2e87d7f30fbf27c64

Observation 194766df-f3fc-4ee5-b3e4-e98f131d6330 · outbound

This paper cites International Conference on Learning Representations (ICLR) , year=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails International Conference on Learning Representations (ICLR) , year=

Reference 22

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source=arxiv_source observed=2026-08-01T17:41:12.841343Z digest=sha256:7ca82a0634a073b5acd6e1fe54c919930a6c09c87da1f1b3f71bebb1f74e72f6

Observation 251709d5-1aed-4f85-8d74-73a188410857 · outbound

This paper cites Forty-second International Conference on Machine Learning , year=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails Forty-second International Conference on Machine Learning , year=

Reference 23

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source=arxiv_source observed=2026-08-01T17:41:12.965470Z digest=sha256:a1b1b3b3cfa3731791eb2a2f89d46cec1c9c34dc1e3a176c8c69d44091f36c71

Observation 160621a4-3af7-4f98-983d-d151546d850b · outbound

This paper cites 2022 , eprint=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails 2022 , eprint=

Reference 24

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source=arxiv_source observed=2026-08-01T17:41:13.105864Z digest=sha256:951989c688633181f3cba9e457e7d7552fa50073478a805293ae85a67f79ccd8

Observation dad4d2ad-fb72-495d-953f-db42ab3e8872 · outbound

This paper cites 2025 , eprint=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails 2025 , eprint=

Reference 25

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source=arxiv_source observed=2026-08-01T17:41:13.262386Z digest=sha256:c94028645f5b4c7af4bff7f826b599d8215b3bf126026ff496c7f224bc00ef69

Observation 168fbcd5-01e3-4266-a2b7-3bd9f5f35ece · outbound

This paper cites How Effective Is Constitutional.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails How Effective Is Constitutional

Reference 26

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source=arxiv_source observed=2026-08-01T17:41:13.351066Z digest=sha256:10adfa9cabde89b05065e48a32fafebf2101863f2278db07fbff54d9207f4c4a

Observation 78455f52-0a7a-4d4b-9322-be3ba563e1c2 · outbound

This paper cites and Lee, Su-In , title =.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails and Lee, Su-In , title =

Reference 27

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source=arxiv_source observed=2026-08-01T17:41:13.451195Z digest=sha256:de337b7f6d47561d261410604f69a762a6bff9496b4bfcc82def879c501c495c

Observation 7946530a-b7a4-4ec9-9245-66d88b5d043f · outbound

This paper cites an unresolved cited work.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails Unresolved cited work

Reference 28

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source=arxiv_source observed=2026-08-01T17:41:13.535163Z digest=sha256:3e12bfb2c710e9cc822f3ce735e39f4e46a0791e34b09a2c4c0950b1fbc8c843

Observation 057402e3-23a1-463a-8b8a-ee67440dfac1 · outbound

This paper cites A Lightweight Explainable Guardrail for Prompt Safety.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails A Lightweight Explainable Guardrail for Prompt Safety

Reference 29

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doi, observed 2026-08-01T17:43:23.574370Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-01T17:41:13.640725Z digest=sha256:b7ce04a6fe9a04256863edd97a72a197d7ad7bf4681536d1a9f1222eccd4546d

Observation 82820fad-4fc0-4810-b74f-18548072b1b3 · outbound

This paper cites Safety Through Reasoning: An Empirical Study of Reasoning Guardrail Models.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails Safety Through Reasoning: An Empirical Study of Reasoning Guardrail Models

Reference 30

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doi, observed 2026-08-01T17:43:23.487421Z

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source=arxiv_source observed=2026-08-01T17:41:13.780317Z digest=sha256:2f5e47f00672a6798522c556c9a8a32f0e3b3362a2600bea9e336b93ee5a9b26

Observation 9298491d-8af5-4135-a3ae-ef323f348575 · outbound

This paper cites 2025 , eprint=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails 2025 , eprint=

Reference 31

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source=arxiv_source observed=2026-08-01T17:41:13.878284Z digest=sha256:59eda107f21b955ca9bdecf8f1851d528f3e745c9fae90501deb06a0d0b5f171

Observation 18eee0ce-992c-46bf-9c57-0d71d60ab61a · outbound

This paper cites T hink G uard: Deliberative Slow Thinking Leads to Cautious Guardrails.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails T hink G uard: Deliberative Slow Thinking Leads to Cautious Guardrails

Reference 32

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source=arxiv_source observed=2026-08-01T17:41:13.988243Z digest=sha256:2e11232169b0e30d3f499fd7b4b7fece44fb858d7bfe062c3fba97fd6433df1f

Observation 607d4dfe-8981-40e2-b709-2995b9194350 · outbound

This paper cites , title =.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails , title =

Reference 33

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source=arxiv_source observed=2026-08-01T17:41:14.102923Z digest=sha256:0d4255c70ede44a6fc07c58b77dd6d6891d7f9f04bc8732d0230e716037d3cf5

Observation 55d8bd89-ac88-4608-8bdf-bd609ae38f3e · outbound

This paper cites 2026 , eprint=.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails 2026 , eprint=

Reference 34

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source=arxiv_source observed=2026-08-01T17:41:14.326098Z digest=sha256:bbceaf5a82efaf008faf4207af35032205626ae2f5656962be6a55feef0769ee

Observation 02561029-08a7-4ef5-bbbc-ca79dd6c9d0c · outbound

This paper cites Faithfulness Tests for Natural Language Explanations.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails Faithfulness Tests for Natural Language Explanations

Reference 35

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source=arxiv_source observed=2026-08-01T17:41:14.497898Z digest=sha256:c48d98a19ea5f68301c39d0e2be4629b5b3d86e8c09d391a2cc114cc40261b01

Observation 548d26ad-aa2f-4f34-8d62-830fcb79cee9 · outbound

This paper cites ERASER : A Benchmark to Evaluate Rationalized NLP Models.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails ERASER : A Benchmark to Evaluate Rationalized NLP Models

Reference 36

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source=arxiv_source observed=2026-08-01T17:41:14.643853Z digest=sha256:b36c1a3393cb145aff12a6edb4ea88e9e8e29b068ba95184df94f677d61a845e

Observation fe2461f4-1f90-4baf-ad6e-d0f7f5362b64 · outbound

This paper cites AEGIS 2.0: A Diverse AI Safety Dataset and Risks Taxonomy for Alignment of LLM Guardrails.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails AEGIS 2.0: A Diverse AI Safety Dataset and Risks Taxonomy for Alignment of LLM Guardrails

Reference 37

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source=arxiv_source observed=2026-08-01T17:41:14.744775Z digest=sha256:b4d9574cf60ad510976e789e262c1742bcac3e4b1faa1292994b3a372ddecb98

Observation 0709e7f2-f4fb-4d5b-a53e-fff1a26230de · outbound

This paper cites T oxic C hat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User- AI Conversation.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails T oxic C hat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User- AI Conversation

Reference 38

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source=arxiv_source observed=2026-08-01T17:41:14.821006Z digest=sha256:353e646942ce809d422f23582427e32d3c4d511af2f454e8ba229b5976409ab2

Observation c0d99aaa-b5a1-4b97-9ada-c2a961c70d6e · outbound

This paper cites N e M o Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails.

A Dual-Hypothesis Reasoning Framework for LLM Guardrails N e M o Guardrails: A Toolkit for Controllable and Safe LLM Applications with Programmable Rails

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-01T17:41:14.893434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T17:41:14.893434Z digest=sha256:fef1607f7a1d5e4bbd32bac1fd4d7b869737ddf273da84f46ede84f53a9b9708

Pith citing papers

No inbound Pith citation observations are available.