Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2503.05731.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T10:29:09.565742Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
4
pith, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation a6ef3c8f-3054-4d30-97e9-7e93f3ea1736 · inbound
How Generative AI Empowers Attackers and Defenders Across the Trust & Safety Landscape cites this paper.
How Generative AI Empowers Attackers and Defenders Across the Trust & Safety Landscape AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 34
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T23:10:26.071510Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation cec9f3ff-7c78-4e8f-a92e-0da682b92f07 · inbound
Adversarial Humanities Benchmark: Results on Stylistic Robustness in Frontier Model Safety cites this paper.
Adversarial Humanities Benchmark: Results on Stylistic Robustness in Frontier Model Safety AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 44
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T12:01:06.452715Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation f01b7fbf-26c3-4dd2-8114-46bfdbe97cfe · inbound
Retrieval with Multiple Query Vectors through Anomalous Pattern Detection cites this paper.
Retrieval with Multiple Query Vectors through Anomalous Pattern Detection AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 51
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T11:21:04.904857Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 4801a16e-23cd-46e8-ad57-4cee300fb83b · inbound
Beyond Fixed Benchmarks and Worst-Case Attacks: Dynamic Boundary Evaluation for Language Models cites this paper.
Beyond Fixed Benchmarks and Worst-Case Attacks: Dynamic Boundary Evaluation for Language Models AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 4
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T20:06:13.644833Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation fa202f1e-c2a3-40a0-973d-319de57059ec · 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 AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 21
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-08T21:39:24.661259Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation e66af813-f6b0-4ab6-a8bc-9b87f23dbd9a · inbound
StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs cites this paper.
StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T05:51:27.376279Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation db573a90-5090-4e7c-9177-03035981bd0a · inbound
StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs cites this paper.
StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T07:22:29.004498Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 28073fe5-0c47-42d2-b035-1b59a0baab60 · inbound
From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI cites this paper.
From AI-Generated Content to Agentic Action: Security and Safety Threats in Generative AI AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 45
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-20T18:08:50.584214Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation bf96173f-7fa0-423c-8fe4-199467748f0d · inbound
GrandGuard: Taxonomy, Benchmark, and Safeguards for Elderly-Chatbot Interaction Safety cites this paper.
GrandGuard: Taxonomy, Benchmark, and Safeguards for Elderly-Chatbot Interaction Safety AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T10:40:00.532077Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 0ac190e0-7e1c-45ad-b02c-b6af840a6912 · inbound
Boiling the Frog: A Multi-Turn Benchmark for Agentic Safety cites this paper.
Boiling the Frog: A Multi-Turn Benchmark for Agentic Safety AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T05:51:07.986970Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 5f66702f-98dd-4a31-bfdb-06bb349e4fb6 · inbound
Boiling the Frog: A Multi-Turn Benchmark for Agentic Safety cites this paper.
Boiling the Frog: A Multi-Turn Benchmark for Agentic Safety AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T06:06:43.210043Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 3e03a6a5-bc49-4674-8f64-267cfb3e490b · inbound
No Safe Dose: How Training Data Drives Unsafe Image Generation cites this paper.
No Safe Dose: How Training Data Drives Unsafe Image Generation AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T13:43:28.542541Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 0133e07d-be5e-4f4d-b821-ca38b69ea7a4 · inbound
LLM Judges Inconsistently Disagree Across Safety Criteria and Harm Categories cites this paper.
LLM Judges Inconsistently Disagree Across Safety Criteria and Harm Categories AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 1
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-01T19:26:00.791513Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 0cb02992-0286-46d6-a0a2-f189af52e861 · inbound
Next-Billion AI Index: The compass for AI utility and adoption in the global majority cites this paper.
Next-Billion AI Index: The compass for AI utility and adoption in the global majority AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 94
- Resolution
- verified exact
- arxiv_id, observed 2026-06-28T19:42:35.732962Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 0bed949c-135b-46a5-be37-15c7151858c8 · inbound
Can Data Work be Reparative? cites this paper.
Can Data Work be Reparative? AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-06-28T02:21:30.122134Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 187eaf4c-0767-4ac9-9c9c-5e6dd4e50397 · inbound
Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting cites this paper.
Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 45
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T01:57:32.632648Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 22711662-d4ec-4f49-b298-e6c63afaf4fa · inbound
FinRED: An Expert-Guided Benchmark Generation and Evaluation Framework for Financial LLM Red-Teaming cites this paper.
FinRED: An Expert-Guided Benchmark Generation and Evaluation Framework for Financial LLM Red-Teaming AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 9
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-04T04:09:34.828204Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation de6bfeee-e473-4212-a9fb-9f11af126d6c · inbound
SafePyramid: A Hierarchical Benchmark for In-context Policy Guardrailing cites this paper.
SafePyramid: A Hierarchical Benchmark for In-context Policy Guardrailing AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T06:34:18.830699Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation b023883e-b65e-41ae-91fc-f0cccb70b193 · inbound
Pluralis v0.1: Towards a Multicultural, Multimodal, Multilingual Benchmark for AI Risk and Reliability cites this paper.
Pluralis v0.1: Towards a Multicultural, Multimodal, Multilingual Benchmark for AI Risk and Reliability AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 124
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-08T13:54:58.599385Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 3dbe5de8-58a5-47f0-ab86-8c15e9dc5dd0 · inbound
The Human Utility Factor: A Computable Welfare Metric That Reframes AI Governance as a Constrained Optimisation Problem cites this paper.
The Human Utility Factor: A Computable Welfare Metric That Reframes AI Governance as a Constrained Optimisation Problem AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T10:29:09.565742Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.