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Paper Citation Record · LEDGER

DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 28 inbound Pith citation observations for arXiv:2306.11698.

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

pith.paper-citation-record.v1
2306.11698 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 28 of 28 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:50:57.942282Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:00:07.922247Z

Reference resolution

0 of 0 outbound references displayed

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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4db0a53b-787f-4a61-bfbe-dc41e1a074b8 · inbound

"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models cites this paper.

"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 84

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metadata mismatch
arxiv_id, observed 2026-05-17T08:39:28.148162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-17T08:39:28.047394Z digest=sha256:0d47e74696aa62e5418fa5f24abbd74f0ad43e30a8a78bf55e350d1df72c692e

Observation fc20f52f-9f66-468a-80ee-38597e7048bb · inbound

GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts cites this paper.

GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 60

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arxiv_id, observed 2026-05-15T06:25:21.086517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T06:25:20.966510Z digest=sha256:e231de425d5fae6007107a280dec16d000138a17f6c4088a7694382025e5ab32

Observation 7133d9f0-4fd0-4606-bbf8-076a4c12371c · inbound

TrustLLM: Trustworthiness in Large Language Models cites this paper.

TrustLLM: Trustworthiness in Large Language Models DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 71

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arxiv_id, observed 2026-05-18T11:17:08.434626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-18T11:17:08.108565Z digest=sha256:1968b31fc6ba7203ab9147e5a568ac05248cdd4ccab520be1442679a2e04fe9e

Observation 0210e987-d74d-4958-b911-c4fc9f579248 · inbound

Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models cites this paper.

Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 110

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verified exact
arxiv_id, observed 2026-05-13T13:43:11.167893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-13T13:43:11.024069Z digest=sha256:897adefff9251718e4adec87d5d3cd44d3b3a3d1bf8b0fafaf28c58de811cf45

Observation 92ba5ce8-989e-458b-a320-97a0b8f4f39a · inbound

Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey cites this paper.

Harmful Fine-tuning Attacks and Defenses for Large Language Models: A Survey DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 154

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arxiv_id, observed 2026-05-23T20:58:25.857135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T20:58:16.237327Z digest=sha256:76c405c217b6aa41a344de81c6452b31c697f4c1759245f2c2067fbcebbbfb7d

Observation 3355686f-75a9-4a76-ab65-d5a32a263c78 · inbound

MJ-VIDEO: Fine-Grained Benchmarking and Rewarding Video Preferences in Video Generation cites this paper.

MJ-VIDEO: Fine-Grained Benchmarking and Rewarding Video Preferences in Video Generation DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 19

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unresolved
no resolver link, observed 2026-08-09T14:50:57.942282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T14:50:57.942282Z digest=sha256:133f28c52ae5f03117f6c2404add20e5249e757c4983b8085eec75018d2a6eca

Observation b219fbce-6ee7-4844-887d-2171c5551971 · inbound

Vulnerability Mitigation for Safety-Aligned Language Models via Debiasing cites this paper.

Vulnerability Mitigation for Safety-Aligned Language Models via Debiasing DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 45

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no resolver link, observed 2026-08-09T13:14:34.113399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T13:14:34.113399Z digest=sha256:96f78fb8990bb564f1ffa83dbacb9f0d1551cdfcaf07d5b3adbbb1500b848bcb

Observation d7cc4c40-8f05-42eb-87e2-f8bab05a5a8d · inbound

Online Aggregation of Trajectory Predictors cites this paper.

Online Aggregation of Trajectory Predictors DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 8

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no resolver link, observed 2026-08-08T13:40:35.417043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:40:35.417043Z digest=sha256:bf3d1e2c92e317c8a2b0baf21a706d0dd66145cead4959c36f9f35454db1ae3c

Observation 02b35cf5-23cd-4abb-b3f2-277592bb7ed5 · inbound

Bridging the Safety Gap: A Guardrail Pipeline for Trustworthy LLM Inferences cites this paper.

Bridging the Safety Gap: A Guardrail Pipeline for Trustworthy LLM Inferences DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 74

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no resolver link, observed 2026-08-08T10:23:06.270812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T10:23:06.270812Z digest=sha256:4892a8f1cfa3d804ac663071a2f675a1cdf6c88557e11d52d2a42af55757a0e3

Observation 0955883b-c37d-4f8a-a42f-19e68cc01e40 · inbound

The Science of Evaluating Foundation Models cites this paper.

The Science of Evaluating Foundation Models DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 80

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unresolved
no resolver link, observed 2026-08-07T23:35:42.869827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T23:35:42.869827Z digest=sha256:63eac86b705a01217d7d4e7cf84fcec071dab676b7e3a7a9c26060b0c59d171b

Observation f2c66839-14a2-4a52-bacd-ebdcea49ebbe · inbound

Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs) cites this paper.

Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs) DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 92

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no resolver link, observed 2026-08-07T13:43:14.525588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:43:14.525588Z digest=sha256:634ad51e7d3283e77cd1c6ec4e5776ea01708071c5f4f56065b48264cbf24000

Observation 5724ed5a-dc59-49c0-9876-5279146e77aa · inbound

Aligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of Race cites this paper.

Aligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of Race DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 65

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unresolved
no resolver link, observed 2026-08-07T12:13:19.236371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:13:19.236371Z digest=sha256:3b7871b34b7fe79883742ad40eea6545f9bbc23a6037682fb0c57143eeb8d2f4

Observation 7a3a8ecf-dfe8-4bb8-b2c8-cbb5955eb159 · inbound

MAGPIE: A dataset for Multi-AGent contextual PrIvacy Evaluation cites this paper.

MAGPIE: A dataset for Multi-AGent contextual PrIvacy Evaluation DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 20

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unresolved
no resolver link, observed 2026-08-06T22:47:24.374725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:47:24.374725Z digest=sha256:5b7a12276dc5a7dc97563a16a0d3757c25c82b174c027f98e9ac82482be94516

Observation f6b5dc35-8a3a-487e-aed3-02e7f334a5dc · inbound

BEAVER: An Efficient Deterministic LLM Verifier cites this paper.

BEAVER: An Efficient Deterministic LLM Verifier DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 51

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metadata mismatch
arxiv_id, observed 2026-05-17T01:58:51.317326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-17T01:58:44.719715Z digest=sha256:572e44657bf215d5cbbfaf58267b28d91f705f1a2bd06677cd4ceb9bb43c2795

Observation ceb2946e-8705-4c94-98f1-18493444a687 · inbound

Beyond Context: Large Language Models' Failure to Grasp Users' Intent cites this paper.

Beyond Context: Large Language Models' Failure to Grasp Users' Intent DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 2

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verified exact
arxiv_id, observed 2026-05-16T20:11:13.660619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-16T20:09:25.827452Z digest=sha256:620dd50621d1b046f8adc745357530274f474afeb0b27e5ba8a9f6db919b0d22

Observation 54ff72f6-97c3-4b95-aafd-7fa693fb6899 · inbound

Framing Instability in LLM Ethical Stance: Auditing Negation Sensitivity in Moral Dilemmas cites this paper.

Framing Instability in LLM Ethical Stance: Auditing Negation Sensitivity in Moral Dilemmas DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 36

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unresolved
no resolver link, observed 2026-08-03T07:03:15.134754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T07:03:15.134754Z digest=sha256:1317ee61b289cd5f39e08735c072ee0e8f24b1ad1d4a18243d4b4b68d89938c8

Observation bc07e591-05ae-4af4-8b5f-c04da5fad9d5 · inbound

Benchmark of Benchmarks: Unpacking Influence and Code Repository Quality in LLM Safety Benchmarks cites this paper.

Benchmark of Benchmarks: Unpacking Influence and Code Repository Quality in LLM Safety Benchmarks DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 121

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metadata mismatch
arxiv_id, observed 2026-05-21T12:10:06.594229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-21T12:09:55.500940Z digest=sha256:feb4596a65128815ddeebf1327bafcae7b6383642e5fe9bcf1a6f96e6e505190

Observation 5cacfdbf-d34c-4df6-b27c-e65578fe7111 · inbound

A Systematic Study of Training-Free Methods for Trustworthy Large Language Models cites this paper.

A Systematic Study of Training-Free Methods for Trustworthy Large Language Models DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 49

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arxiv_id, observed 2026-05-10T08:22:37.342422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T08:19:42.671690Z digest=sha256:0180ee0a178e20f503cefead4a52c4ea3a3a26ad7085d0b05105ea1761f608a3

Observation b1668728-b10d-40db-a4cc-b931a3c371ba · inbound

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization cites this paper.

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 13

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arxiv_id, observed 2026-05-11T17:56:05.484799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T16:57:49.396570Z digest=sha256:7a86b88372b2092eef9d371205ca023eeeafc4daa3a05988896eadc5a02292e2

Observation a1eb1751-c4bf-4f96-a289-06bf31a45ae2 · inbound

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization cites this paper.

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 13

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verified exact
arxiv_id, observed 2026-05-12T07:21:26.471179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-12T03:26:54.426050Z digest=sha256:be2a252566d8656a32179fc0a79e5f1f25dea7c776af7915012cbfbaa1986bb4

Observation 527ad1e2-4ce9-4b72-8ab5-20ce8685977b · inbound

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization cites this paper.

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 13

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verified exact
arxiv_id, observed 2026-05-13T07:12:28.807805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-13T07:08:39.328446Z digest=sha256:bb5001b322279066d6ebb5a3464f8ac6a965d0ce89f15888c8db03b816c30fda

Observation 4ee47a55-5feb-410b-9161-a7ba17536758 · inbound

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization cites this paper.

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 13

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unresolved
no resolver link, observed 2026-08-02T14:53:23.078669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T14:53:23.078669Z digest=sha256:18d01c24728e66ee0013155ee365ee2737c4d5a5456d3d14e98008e9804d4cd4

Observation 47752c70-ef9a-488e-afc3-5eb0c072034a · inbound

CXR-ContraBench: Benchmarking Negated-Option Attraction in Medical VLMs cites this paper.

CXR-ContraBench: Benchmarking Negated-Option Attraction in Medical VLMs DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 32

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metadata mismatch
arxiv_id, observed 2026-05-11T18:41:09.433527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-08T14:49:53.357083Z digest=sha256:aabb60af3fbdc6d8e45bc5aa8f107838e2de9c4e6106dca24bbb268b1abd54fd

Observation af6d18f2-de9f-480a-bd80-a9f876453f20 · inbound

Reducing Political Manipulation with Consistency Training cites this paper.

Reducing Political Manipulation with Consistency Training DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 38

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verified exact
arxiv_id, observed 2026-05-22T05:34:40.195959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-22T05:32:19.312335Z digest=sha256:9fc0637fd1c3d3e70c501384bd47c950e1a492d9283f2c70cf6013e3f6585575

Observation c2b48358-6547-4d20-86cc-21c5a292e836 · inbound

Reducing Political Manipulation with Consistency Training cites this paper.

Reducing Political Manipulation with Consistency Training DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 38

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verified exact
arxiv_id, observed 2026-06-30T16:54:58.737164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T16:49:16.542582Z digest=sha256:44b5d02c0b805f729c339dd03f3544c3fbad787f8697bb2a064f6428247eb1b9

Observation c9077dec-4165-4e35-80a2-6375abd02c46 · inbound

A Paired Testing Protocol for Batch-Conditioned Refusal Robustness in LLM Serving cites this paper.

A Paired Testing Protocol for Batch-Conditioned Refusal Robustness in LLM Serving DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 22

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verified exact
arxiv_id, observed 2026-06-29T18:03:47.862186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-29T18:03:19.798168Z digest=sha256:da35700c67381d302c52b16203fe2a14d746319d0edb8426d098475889f42c63

Observation 3c7c38b7-3822-42fc-92a9-47f016b3dfdb · inbound

Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges cites this paper.

Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Challenges DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 20

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T01:27:30.922163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-27T16:33:28.848573Z digest=sha256:f9518b0e8c59146d4e9da087f5fdad095b56db0d4d7821a2eca2e332c7be2280

Observation 6304b4f6-9dad-46ed-ae59-f3c6de2072be · inbound

Do Encoders Suffice? A Systematic Comparison of Encoder and Decoder Safety Judges for LLM Adversarial Evaluation cites this paper.

Do Encoders Suffice? A Systematic Comparison of Encoder and Decoder Safety Judges for LLM Adversarial Evaluation DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

Reference 11

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metadata mismatch
arxiv_id, observed 2026-07-04T20:00:07.924444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-25T20:55:44.549142Z digest=sha256:6da80ed96ba930665a945b36428247b28d5d266db9b26027623c66add0c177f6