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Paper Citation Record · LEDGER

A Dual-Hypothesis Reasoning Framework for LLM Guardrails

As of 8 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 2026-08-08T06:32:00.761636+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=sha256:75d8834ff2a560bf36cb36c4d8fddd84c6abb95a5cece5a42682e34643744aef

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:c6843f28b7b64d45e876d5d6e3fbc92051ce34b084a78dc091dba73a8b27738c

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=sha256:88386ebd9d89f93f497403426b6a5f932e0b007772d37957a548c658da7ae775

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:ad539ed480cf68ca15c478ced736629890d792016bede7b1d17537c65899dce0

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=sha256:3597d5618519dbb47020beccefb4b7ff59b9f4ed6b77feb689eb4fc1e940bd09

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=sha256:235477e4e595ba04fddde677762435d6ec36ddcb4522d058c0ecae44262424f3

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:a34b91f811b7c8e73fb9f4eff5d34d3e59eb33b5ef4ff848682560462fb73f07

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:d55830022b80149c150b27b2b1ee4022e55d865fc5fe34041d6a8ac52a0938c1

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=sha256:4c716780d5efef85a7426eaba523a0330cda3e49e3f69ea520c18661589a31e6

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=sha256:6a94e99d767a89f6e3535de939d9e74e4f72facb96d0cb3801f2257e84929914

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:d83dab9dcb4371d56c21c7da685f2daae29a3317724c5300cba4fd50fb4baa7a

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=sha256:2b12a188cde5a6ec22d1a844c5f8c8242999dad91f51c16d337a92d5c9af02ee

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=sha256:2acae24ce20ca42ad09a6e903a91e56428eb5f414bd5ef5f0bf7a644ab47b447

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:d07205b74a8ea59a892b6e0b4387a9c6bc228077fbd12b72f27c9b09cc581d99

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:d3342fa77383228158e3e5d537ce42d05abf89e0e9501e353b526b26d58db49f

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:e8215f8216530f8bf48f409f02d62063b01f967bfa96b19c9cde84d305b07393

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:f84fe7505deddc403c12a987b0cf4702c3ede27d95b517561733039d33815fa1

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:a07c1caf6eca912d6fe30d6675ebf6834c65c77aba47652c4d721ab375740e2b

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=sha256:0989f2d66b367e23d3ac4de6cd2bd529023eb942a01652b2d790c49d053f3d10

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=sha256:93f2a22e6c9b5fb241e4fb311bbed1cd9c085d38b3221e6ac37c4cd316908ae5

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:aef2945773a6a703d11eceee9ae38d96580182fc66b1bcd79646699b6603f21f

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=sha256:6908f8ed76a2e66bb984649525d738bfb99c979a9a5d6f7d7fc97f1e1f2ae00f

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:c61d31270677641f1d63fefb0f53590fc1ffc84a3112a0284f028a4c366fb54f

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=sha256:30adc0162a41b822446141f42e0e20ae6aac46dfd2f5f4ee6593ac5043bcbaf3

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=sha256:48c2789f9cd9571cc6596e3752774ca9a2477195d76277b1e2638fbdc38a9099

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:dfcce0132fae6b375a9b86495c248a20c8412025864593408e5ba15cf13fd501

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=sha256:521a0685e5f3d792ee3f2730e96da64dca41438608beea7a0f75d96d0499f3cf

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=sha256:4ac56b5c6c8867b778590c4c5753393e792b377fa6a009736e1d03b0cc0da4e1

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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source=arxiv_source observed=2026-08-01T17:41:13.640725Z digest=sha256:5415ee2da0fa3726dd4540827a3b4c18dd4c4e5a761a4891dda162ba40d7751b

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:ec6ea0418398bb9540b71769696c2a8e57e4e732adf90c28ae9c0ca38c2d648b

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:c75a9029f7fac69221fe7c4acc41f2e92148620c0946b6c0823601a63eaea528

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=sha256:286b4169ca946ffaf87cf6e57f6562c1f68556c1da99fe7b3eeefd6ce19c72bd

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:a51e91e4815298779e7d38c3eb6f9199a483d22c9d0b6f90dd7b16760bfbef92

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=sha256:804e2f26c617ee20202b265571d7083f0001dab09196c1dadaca1befe6538ab9

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=sha256:91968a9bc5a5eb774ba38290623cf4ee3a18461c7953473c4c4bc7e55738142a

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=sha256:3cf69ee8979f063c3524127a90083519925767067aa1763764c39b1ce9a0984c

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:ce9884235e8729390839a056f720b877e45ce015bfb103ee9a4d89e91198e826

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=sha256:20c2056300b0eb0b2297d188ffe32cde20ae39c8c8cd1899941516c973a51c0f

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=sha256:661087b0bc46475df494c48520863b8121906d78ae68221351ed5ff410810516

Pith citing papers

No inbound Pith citation observations are available.