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Paper Citation Record · LEDGER

SafetyBench: Evaluating the Safety of Large Language Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 35 inbound Pith citation observations for arXiv:2309.07045.

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

pith.paper-citation-record.v1
2309.07045 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 35 of 35 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 35 of 35 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:52:42.777127Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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  • malformed identifier0
  • metadata mismatch0

External citation measurements

19
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 9af1f249-4f1a-4f1d-b322-b462cff38150 · inbound

ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools cites this paper.

ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools SafetyBench: Evaluating the Safety of Large Language Models

Reference 56

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verified exact
arxiv_id, observed 2026-05-11T08:08:09.724948Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-11T08:08:09.444352Z digest=sha256:b32762a0e507629e1f1da3ee63584f2e2d9484fb1deb9dc55b9a1ddc45e03ab0

Observation fffc98aa-9840-491a-89b7-8920516259bb · inbound

Jailbreak Attacks and Defenses Against Large Language Models: A Survey cites this paper.

Jailbreak Attacks and Defenses Against Large Language Models: A Survey SafetyBench: Evaluating the Safety of Large Language Models

Reference 115

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arxiv_id, observed 2026-05-15T02:20:44.493758Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-15T02:20:44.368219Z digest=sha256:713fc88a0e4547261bdb56074672405e5bbe749f33b7ede505e574acff891ee1

Observation a444735e-47da-4991-9cee-16ece478ec49 · inbound

SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use cites this paper.

SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use SafetyBench: Evaluating the Safety of Large Language Models

Reference 57

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no resolver link, observed 2026-08-07T14:52:31.148227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:52:31.148227Z digest=sha256:5300e28b97482b8b8ea46a81526d0b93a2bd1280b9d467c801ab19be6b35a371

Observation 61a96958-5215-45cd-a6be-9a719353b435 · inbound

Language Matters: How Do Multilingual Input and Reasoning Paths Affect Large Reasoning Models? cites this paper.

Language Matters: How Do Multilingual Input and Reasoning Paths Affect Large Reasoning Models? SafetyBench: Evaluating the Safety of Large Language Models

Reference 23

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no resolver link, observed 2026-08-07T14:52:42.777127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:52:42.777127Z digest=sha256:5ee4a0b877f0603ad917f6506669fb730fb1001428676d234cd9a0bb904f314c

Observation fad597c3-b1dd-45f1-b0ff-93fac7ab0d79 · inbound

LLM-based HSE Compliance Assessment: Benchmark, Performance, and Advancements cites this paper.

LLM-based HSE Compliance Assessment: Benchmark, Performance, and Advancements SafetyBench: Evaluating the Safety of Large Language Models

Reference 50

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unresolved
no resolver link, observed 2026-08-07T13:00:59.091799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:00:59.091799Z digest=sha256:5a4074e1a298413e821ee4928ba6f148108f5f5891588283da86e7d85bc2d2f6

Observation fe0f0a71-571a-48fb-be9b-dc635c57f7cb · inbound

USB: A Comprehensive and Unified Safety Evaluation Benchmark for Multimodal Large Language Models cites this paper.

USB: A Comprehensive and Unified Safety Evaluation Benchmark for Multimodal Large Language Models SafetyBench: Evaluating the Safety of Large Language Models

Reference 44

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unresolved
no resolver link, observed 2026-08-07T14:15:16.805123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:15:16.805123Z digest=sha256:19b954f6cf531146635e02a4f710aea1d54ccf38e7c343fe83426381d2af4bb0

Observation 8d2f4893-47b3-4137-af3c-f0aa5f4dec6e · inbound

Large Language Models Often Know When They Are Being Evaluated cites this paper.

Large Language Models Often Know When They Are Being Evaluated SafetyBench: Evaluating the Safety of Large Language Models

Reference 3

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malformed identifier
no resolver link, observed 2026-08-07T13:16:15.735454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:16:15.735454Z digest=sha256:f7609645c64576e571bd0ac3edaca656bbba0a9912bd76c8c2f47b64c9efc3d4

Observation e8ab284a-3778-40f8-9bf3-8bd0b1e2834d · inbound

MTCMB: A Multi-Task Benchmark Framework for Evaluating LLMs on Knowledge, Reasoning, and Safety in Traditional Chinese Medicine cites this paper.

MTCMB: A Multi-Task Benchmark Framework for Evaluating LLMs on Knowledge, Reasoning, and Safety in Traditional Chinese Medicine SafetyBench: Evaluating the Safety of Large Language Models

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T11:50:54.874110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:50:54.874110Z digest=sha256:7280174c9f649321a407a95affc0d982dfd967a3d5fb4412ca4bdea8a4d54d77

Observation a58398f4-79b8-4b32-8d86-a01a50d3b514 · inbound

SafeCoT: Improving VLM Safety with Minimal Reasoning cites this paper.

SafeCoT: Improving VLM Safety with Minimal Reasoning SafetyBench: Evaluating the Safety of Large Language Models

Reference 27

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no resolver link, observed 2026-08-07T05:19:02.714368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:19:02.714368Z digest=sha256:8dd7008434fc28b6292e85d85d6484ff3646f2e8a760115a05f565f17ae18571

Observation 5152cbc0-bd5b-4048-866a-a5f5ef1b9b59 · inbound

PL-Guard: Benchmarking Language Model Safety for Polish cites this paper.

PL-Guard: Benchmarking Language Model Safety for Polish SafetyBench: Evaluating the Safety of Large Language Models

Reference 27

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unresolved
no resolver link, observed 2026-08-06T23:49:54.958475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:49:54.958475Z digest=sha256:464c61380dc4730f4b772a0253f1ba75d10d2e0a6a585a98bb2359bb89c6e0e7

Observation ff373dd7-bf4a-417b-ab03-8b441f3d62af · inbound

Informing AI Risk Assessment with News Media: Analyzing National and Political Variation in the Coverage of AI Risks cites this paper.

Informing AI Risk Assessment with News Media: Analyzing National and Political Variation in the Coverage of AI Risks SafetyBench: Evaluating the Safety of Large Language Models

Reference 5

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malformed identifier
no resolver link, observed 2026-08-06T10:31:06.665914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T10:31:06.665914Z digest=sha256:7379a5ff9e19f9a631aee95534a782d2e7edd71335e94e50cd32664d00dbe1d7

Observation 99b6e058-53b0-43ab-ba9a-6c69bde88bcc · inbound

Observation of momentum dependent charge density wave gap in EuTe4 cites this paper.

Observation of momentum dependent charge density wave gap in EuTe4 SafetyBench: Evaluating the Safety of Large Language Models

Reference 51

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no resolver link, observed 2026-08-05T22:45:17.057354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T22:45:17.057354Z digest=sha256:be233d12810d2b8ab00bb035e61f6276971bdc01b5dbca29ea4022d6a80af067

Observation 20151091-1b09-439b-8963-3e7fdbb5a3cc · inbound

GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models cites this paper.

GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models SafetyBench: Evaluating the Safety of Large Language Models

Reference 51

Resolution
verified exact
arxiv_id, observed 2026-05-11T17:50:08.558363Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-11T17:50:08.399160Z digest=sha256:e5665f0f8b51078b6b4012d7e32ea9949a837ea30e62fff2dd887986450e18cb

Observation 13039f77-573b-488c-935f-c28a25313167 · inbound

Behind the Mask: Benchmarking Camouflaged Jailbreaks in Large Language Models cites this paper.

Behind the Mask: Benchmarking Camouflaged Jailbreaks in Large Language Models SafetyBench: Evaluating the Safety of Large Language Models

Reference 33

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unresolved
no resolver link, observed 2026-08-05T05:29:20.767922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T05:29:20.767922Z digest=sha256:b355ce08bddc5f2f56cba1b57bec22b00b9d17de8b3459e68ddab1e69b924f61

Observation 0b4b09e4-fb46-4bf9-9358-e27503e3e903 · inbound

Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm cites this paper.

Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm SafetyBench: Evaluating the Safety of Large Language Models

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-04T22:33:25.711468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:33:25.711468Z digest=sha256:82ce839e830315d48a81db0480d17de3e27b242a10482f840390327f20868161

Observation 4d32f22f-ce66-49cd-b9fd-ac8d1e2ec432 · inbound

YouthSafe: A Youth-Centric Safety Benchmark and Safeguard Model for Large Language Models cites this paper.

YouthSafe: A Youth-Centric Safety Benchmark and Safeguard Model for Large Language Models SafetyBench: Evaluating the Safety of Large Language Models

Reference 42

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unresolved
no resolver link, observed 2026-08-04T19:55:56.607844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T19:55:56.607844Z digest=sha256:072fcbf848ac6334b0e7cc76d34dba68e0c1ee7175e7b1815a8dacae726d8d34

Observation 31ff1476-4f45-4a69-b5f3-fc965d7af2ae · inbound

Controlling the Risk of Corrupted Contexts for Language Models via Early-Exiting cites this paper.

Controlling the Risk of Corrupted Contexts for Language Models via Early-Exiting SafetyBench: Evaluating the Safety of Large Language Models

Reference 48

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unresolved
no resolver link, observed 2026-08-04T12:45:26.981751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T12:45:26.981751Z digest=sha256:051abe723dcc23a367f3cae62f575c4c287a82bac18e7bb36133e0de8ef7ce54

Observation 29ed8296-e9a6-4df2-a9b4-323305286cc8 · inbound

Safety Game: Inference-Time Alignment of Black-Box LLMs via Constrained Optimization cites this paper.

Safety Game: Inference-Time Alignment of Black-Box LLMs via Constrained Optimization SafetyBench: Evaluating the Safety of Large Language Models

Reference 25

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unresolved
no resolver link, observed 2026-08-04T10:40:58.482981Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T10:40:58.482981Z digest=sha256:9e22ea9513a49f067130bcffdd3bec922d43351984e18277b8630c753482939c

Observation a7f5c285-c784-4bbb-ace5-834a5f3a7e3e · inbound

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

Beyond Context: Large Language Models' Failure to Grasp Users' Intent SafetyBench: Evaluating the Safety of Large Language Models

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-16T20:11:13.678082Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-16T20:09:25.827452Z digest=sha256:3118a9c584d5c1dfdcf727fae1eb310b8c926b34736233cbb35afc2cb200f4a0

Observation f5fbc10e-70a4-445a-8b2a-2e2e99205d45 · inbound

Breakdowns in Conversational AI: Interactional Failures in Emotionally and Ethically Sensitive Contexts cites this paper.

Breakdowns in Conversational AI: Interactional Failures in Emotionally and Ethically Sensitive Contexts SafetyBench: Evaluating the Safety of Large Language Models

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-05-13T19:48:11.316619Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-13T19:46:56.456625Z digest=sha256:432100a8e7c410a9da538b44bc57c5dc24bd19e321f52d194f27d650e2f7dc99

Observation c903e576-4032-44b3-b529-1bafc14e20cf · inbound

VoxSafeBench: Not Just What Is Said, but Who, How, and Where cites this paper.

VoxSafeBench: Not Just What Is Said, but Who, How, and Where SafetyBench: Evaluating the Safety of Large Language Models

Reference 13

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verified exact
arxiv_id, observed 2026-05-10T10:24:22.098823Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-10T10:19:28.041282Z digest=sha256:d9dac9b7d75ca627c63a870a269cf93519817e31640285fb1956b5304368947c

Observation f1c6253d-ff1e-4731-a693-4590896ba80f · inbound

Benign Fine-Tuning Breaks Safety Alignment in Audio LLMs cites this paper.

Benign Fine-Tuning Breaks Safety Alignment in Audio LLMs SafetyBench: Evaluating the Safety of Large Language Models

Reference 29

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metadata mismatch
arxiv_id, observed 2026-05-10T08:02:24.878897Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-10T08:01:25.938248Z digest=sha256:532435687a34934ef1fb98081c27f19574cbcee0fb6d88df9c3389b8dd41487c

Observation d67dfdc2-8794-4a7b-a329-e7920a0585f6 · inbound

How Sensitive Are Safety Benchmarks to Judge Configuration Choices? cites this paper.

How Sensitive Are Safety Benchmarks to Judge Configuration Choices? SafetyBench: Evaluating the Safety of Large Language Models

Reference 17

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arxiv_id, observed 2026-05-11T21:56:35.407538Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-08T03:41:32.338529Z digest=sha256:1535917f3031d5d74157d095c4259a13e479daae3c6588baf0c782eb5508ecef

Observation 210eaf0c-c35b-4cd5-bb8a-6d26961a24bd · inbound

When No Benchmark Exists: Validating Comparative LLM Safety Scoring Without Ground-Truth Labels cites this paper.

When No Benchmark Exists: Validating Comparative LLM Safety Scoring Without Ground-Truth Labels SafetyBench: Evaluating the Safety of Large Language Models

Reference 20

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arxiv_id, observed 2026-05-08T21:39:24.707089Z

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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=2026-05-08T12:07:02.778631Z digest=sha256:40f8d5910a42f3bbb8216acbe13ddb29c9345bdcacda4ad4bc80e1d9f44e722a

Observation e61ae913-d454-4b05-90b8-92a50e6ce9fd · inbound

Navigating the Sea of LLM Evaluation: Investigating Bias in Toxicity Benchmarks cites this paper.

Navigating the Sea of LLM Evaluation: Investigating Bias in Toxicity Benchmarks SafetyBench: Evaluating the Safety of Large Language Models

Reference 34

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T05:31:23.839851Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-12T05:28:45.453455Z digest=sha256:b30adac784970620c684b5fae09bbb67b23427e3447ef1664c19001c35b8d7b2

Observation d7668e7c-4541-4545-b656-62b918729385 · inbound

Boiling the Frog: A Multi-Turn Benchmark for Agentic Safety cites this paper.

Boiling the Frog: A Multi-Turn Benchmark for Agentic Safety SafetyBench: Evaluating the Safety of Large Language Models

Reference 92

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verified exact
arxiv_id, observed 2026-05-22T05:51:08.043818Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-22T05:50:28.114140Z digest=sha256:3a1edba10cf845737c4adffd32b83a33d29ba7d853fa4f7717cd49d2302cb6ad

Observation 4d77745e-3327-4ef7-8c07-7c5138ffe310 · inbound

Boiling the Frog: A Multi-Turn Benchmark for Agentic Safety cites this paper.

Boiling the Frog: A Multi-Turn Benchmark for Agentic Safety SafetyBench: Evaluating the Safety of Large Language Models

Reference 92

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verified exact
arxiv_id, observed 2026-05-25T06:06:42.916232Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-25T06:05:27.736494Z digest=sha256:1278dc42302c3e5329c20ff3903de696095cc8ccc195ebb00765b9bd57bbcd8d

Observation 2c26ebce-5e82-406e-aaa1-46c1469485b6 · inbound

Benchmarking Open-Source Safety Guard Models: A Comprehensive Evaluation cites this paper.

Benchmarking Open-Source Safety Guard Models: A Comprehensive Evaluation SafetyBench: Evaluating the Safety of Large Language Models

Reference 15

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unresolved
no resolver link, observed 2026-07-12T23:33:25.778345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T23:33:25.778345Z digest=sha256:c07f600b6f9f718924c567813dabb5d3b1150fcf7b786be36689579fc4e4e90c

Observation 7859f42a-5e2b-46f0-acb9-e58f687c05b2 · inbound

Efficient Safety Benchmarking via Item Response Theory cites this paper.

Efficient Safety Benchmarking via Item Response Theory SafetyBench: Evaluating the Safety of Large Language Models

Reference 20

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metadata mismatch
arxiv_id, observed 2026-07-01T15:55:48.988875Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-07-01T15:51:00.484343Z digest=sha256:8e4404f07d9eb6144e716566b9d84ddb79fcfdec9c1c47554a19cbffcd399599

Observation 874e7ef1-95b4-4fd1-a4cd-b738bf512552 · inbound

Yuvion LLM: An Adversarially-Aware Large Language Model for Content And AI Safety cites this paper.

Yuvion LLM: An Adversarially-Aware Large Language Model for Content And AI Safety SafetyBench: Evaluating the Safety of Large Language Models

Reference 40

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verified exact
arxiv_id, observed 2026-06-29T00:52:55.751302Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-06-29T00:46:03.210076Z digest=sha256:7a600e09c50238e22e2f0b81c34b190610008bf6d6f6f2073b64dd4074829c3d

Observation 4114ba8d-56ca-476e-bbb9-665ba29f2bbd · inbound

Two AI Metrics Diverged: Will it Make All the Difference? cites this paper.

Two AI Metrics Diverged: Will it Make All the Difference? SafetyBench: Evaluating the Safety of Large Language Models

Reference 47

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metadata mismatch
arxiv_id, observed 2026-07-02T12:36:56.167767Z

Source-reported events for the cited work

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

source=arxiv_source observed=2026-07-02T12:29:24.439779Z digest=sha256:f6675d36cc28e52f2b117fa23712bc8bda9607372a6fee9d4183eb1897a74599

Observation 501a2773-fc1d-4157-ae95-cde76bae5975 · inbound

Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI cites this paper.

Unsafe at any AUC: Unlearned Lessons from Sociotechnical Disasters for Responsible AI SafetyBench: Evaluating the Safety of Large Language Models

Reference 176

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unresolved
no resolver link, observed 2026-08-02T02:22:11.309343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T02:22:11.309343Z digest=sha256:17db8b7cbe72b9a55da3b82d6e1e2a813ef46573aa3764c1059f26e3b9cb1082

Observation 35546b27-3658-41db-ad14-ecb340c7aaee · inbound

What AI Red-Team Evaluations Can and Cannot Prove cites this paper.

What AI Red-Team Evaluations Can and Cannot Prove SafetyBench: Evaluating the Safety of Large Language Models

Reference 17

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unresolved
no resolver link, observed 2026-08-01T06:54:48.369854Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T06:54:48.369854Z digest=sha256:b4aaf16622d2d572e820e82b3d3dd78453b006a1a2b462ae9761be9e32f249e0

Observation e6acd021-14d3-4938-a9b6-830ddaa8c29f · inbound

AIR-BENCH Live: An Evolving Safety Benchmark for Foundation Models cites this paper.

AIR-BENCH Live: An Evolving Safety Benchmark for Foundation Models SafetyBench: Evaluating the Safety of Large Language Models

Reference 10

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no resolver link, observed 2026-08-02T08:30:02.855362Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T08:30:02.855362Z digest=sha256:3f72d0f1d5074a703c64be2ab8c52d1ff1d3a1d5cceb23da78e850abaf870bde

Observation 2b2a3d9a-93ff-40a9-b21e-f596346e3570 · inbound

Chain-of-Models: Cross-Model Auditing for Bias-Robust LLM Judges cites this paper.

Chain-of-Models: Cross-Model Auditing for Bias-Robust LLM Judges SafetyBench: Evaluating the Safety of Large Language Models

Reference 38

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no resolver link, observed 2026-08-03T00:55:22.021201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-03T00:55:22.021201Z digest=sha256:349ba16a2faa540723b4cfee231c4019a3008f794bdf30588ea1e6e12bd02f8c