Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting | Paper Citation Record

Observation 7b245c6e-5c7c-42ae-8c18-1581876bfa32 · outbound

This paper cites Developing and maintaining an open- source repository of AI evaluations: Challenges and insights.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Developing and maintaining an open- source repository of AI evaluations: Challenges and insights

Reference 1

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This paper cites When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

Reference 2

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This paper cites Audit and Assurance of AI Algorithms: A framework to ensure ethical algorithmic practices in Artificial Intelligence.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Audit and Assurance of AI Algorithms: A framework to ensure ethical algorithmic practices in Artificial Intelligence

Reference 3

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Lessons from the trenches on evaluating machine-learning systems in materials science.Computational Materi- als Science, 2025

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This paper cites AllMetrics: A Unified Python Library for Standardized Metric Evaluation and Robust Data Validation in Machine Learning.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting AllMetrics: A Unified Python Library for Standardized Metric Evaluation and Robust Data Validation in Machine Learning

Reference 5

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Arnstein

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This paper cites Comparison of AI models across intelligence, performance, and price,.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Comparison of AI models across intelligence, performance, and price,

Reference 7

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Unresolved cited work

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This paper cites AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources

Reference 9

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Leak, cheat, repeat: Data contamination and evaluation malpractices in closed-source LLMs

Reference 10

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This paper cites When Fairness Isn't Statistical: The Limits of Machine Learning in Evaluating Legal Reasoning.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting When Fairness Isn't Statistical: The Limits of Machine Learning in Evaluating Legal Reasoning

Reference 11

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This paper cites Every eval ever: Toward a common language for AI eval reporting.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Every eval ever: Toward a common language for AI eval reporting

Reference 12

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This paper cites arXiv preprint arXiv:2511.04703; presented at Neurips 2025 , year=.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting arXiv preprint arXiv:2511.04703; presented at Neurips 2025 , year=

Reference 13

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This paper cites Absolute Evaluation Measures for Machine Learning: A Survey.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Absolute Evaluation Measures for Machine Learning: A Survey

Reference 14

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This paper cites Open llm leaderboard (2023- 2024).

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Open llm leaderboard (2023- 2024)

Reference 15

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Lessons from the Trenches on Reproducible Evaluation of Language Models

Reference 16

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This paper cites A metrological framework for uncertainty evaluation in machine learning classification models.Metrologia, 2025.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting A metrological framework for uncertainty evaluation in machine learning classification models.Metrologia, 2025

Reference 17

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This paper cites The impact of standardisation and standards on innovation.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting The impact of standardisation and standards on innovation

Reference 18

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This paper cites Assessing ai: Surveying the spectrum of approaches to understanding and auditing ai systems, 2025.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Assessing ai: Surveying the spectrum of approaches to understanding and auditing ai systems, 2025

Reference 19

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Evaluation for change

Reference 20

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This paper cites Bordes, C.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Bordes, C

Reference 21

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Unresolved cited work

Reference 22

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Cohn, and Jose Hernandez- Orallo

Reference 23

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting best fit

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Black-box access is insufficient for rigorous AI audits, 2024

Reference 25

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting ISBN 978-1-4503-7110-0

Reference 26

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Managing misuse risk for dual-use foundation models

Reference 27

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Test & Evaluation Best Practices for Machine Learning-Enabled Systems

Reference 28

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This paper cites Evaluating machine expertise: How graduate students develop frameworks for assessing GenAI content, 2025.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Evaluating machine expertise: How graduate students develop frameworks for assessing GenAI content, 2025

Reference 29

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Gonzalez, and Ion Stoica

Reference 30

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Collins, Karel G

Reference 31

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This paper cites Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem

Reference 32

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Evalcards: A framework for standardized evaluation reporting, 2025

Reference 33

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Nahab, and Xiao Hu

Reference 34

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Reconsideration on evaluation of machine learning models in continuous monitoring using wearables

Reference 35

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Introducing Epoch AI’s AI benchmarking hub, 2024

Reference 36

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Can we trust AI benchmarks? An interdisci- plinary review of current issues in AI evaluation

Reference 37

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting The general-purpose AI code of practice: Safety & security chapter, July 2025

Reference 38

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting The general-purpose AI code of practice: Trans- parency chapter, July 2025

Reference 39

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting EvalEval: Every eval ever shared task, 2024

Reference 40

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Good practices for evaluation of machine learning systems

Reference 41

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Frontier capability assessment

Reference 42

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Datasheets for datasets.Commun

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This paper cites Repairing the cracked foundation: A survey of obstacles in evaluation practices for generated text.Journal of Artificial Intelligence Research, 2023.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Repairing the cracked foundation: A survey of obstacles in evaluation practices for generated text.Journal of Artificial Intelligence Research, 2023

Reference 44

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons

Reference 45

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This paper cites Stress-Testing Capability Elicitation With Password-Locked Models.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Stress-Testing Capability Elicitation With Password-Locked Models

Reference 46

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Olmes: A standard for language model evaluations

Reference 47

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Gupta, Jessica Hullman, and Hari Subramonyam

Reference 48

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting System Cards for AI-Based Decision-Making for Public Policy

Reference 49

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This paper cites Empirical Privacy Evaluations of Generative and Predictive Machine Learning Models -- A review and challenges for practice.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Empirical Privacy Evaluations of Generative and Predictive Machine Learning Models -- A review and challenges for practice

Reference 50

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Bernstein, and Mykel John Kochenderfer

Reference 51

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting A sober look at progress in language model reasoning: Pitfalls and paths to reproducibility

Reference 52

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Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Auto-benchmarkcard: Automated synthesis of benchmark documentation

Reference 53

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This paper cites Values in the Wild: Discovering and Analyzing Values in Real-World Language Model Interactions.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Values in the Wild: Discovering and Analyzing Values in Real-World Language Model Interactions

Reference 54

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This paper cites Evaluation gaps in machine learning practice.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Evaluation gaps in machine learning practice

Reference 55

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This paper cites Rethinking Machine Learning Model Evaluation in Pathology.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Rethinking Machine Learning Model Evaluation in Pathology

Reference 56

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This paper cites Deprecating benchmarks: Criteria and framework.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Deprecating benchmarks: Criteria and framework

Reference 57

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This paper cites Cantrell, Keiran Peng, Thanh Huy Pham, Christopher A.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Cantrell, Keiran Peng, Thanh Huy Pham, Christopher A

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Observation 8c8c2b13-2bfe-4c2e-9801-167b8ec3a8a8 · outbound

This paper cites Benchmark profiling: Mechanistic diagnosis of LLM benchmarks, 2025.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Benchmark profiling: Mechanistic diagnosis of LLM benchmarks, 2025

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-07-03T02:07:33.296898Z

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 3525567a-a7c4-416d-b5e0-5c026955eac2 · outbound

This paper cites Had- field, Lukas Heim, Marianela Rodriguez, Jonas B.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Had- field, Lukas Heim, Marianela Rodriguez, Jonas B

Reference 60

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abb5f37e-6155-4945-96e0-c23b990aeb37 · outbound

This paper cites Richard Landis and Gary G.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Richard Landis and Gary G

Reference 61

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a58b9389-fa75-416b-b92b-fe1336010982 · outbound

This paper cites Towards explainable evaluation metrics for machine translation.Journal of Machine Learning Research, 2024.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Towards explainable evaluation metrics for machine translation.Journal of Machine Learning Research, 2024

Reference 62

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c99d7223-2015-4d88-a724-58467ba04a48 · outbound

This paper cites Frangi, Antonio R.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Frangi, Antonio R

Reference 63

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b84f8a9e-b3a1-44d0-a18d-336e07395aad · outbound

This paper cites Holistic Evaluation of Language Models.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Holistic Evaluation of Language Models

Reference 64

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T02:07:33.254030Z

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 fa987c16-36fc-4970-98eb-7890c5f31ba9 · outbound

This paper cites Manning, Christopher Ré, Diana Acosta-Navas, Drew A.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Manning, Christopher Ré, Diana Acosta-Navas, Drew A

Reference 65

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab6b7e59-b904-4f46-b24d-3c8807e41fa3 · outbound

This paper cites Are we learning yet? a meta review of evaluation failures across machine learning.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Are we learning yet? a meta review of evaluation failures across machine learning

Reference 66

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7a96ef9-77b9-4134-88d0-e5db5a517309 · outbound

This paper cites A safe harbor for AI evaluation and red teaming.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting A safe harbor for AI evaluation and red teaming

Reference 67

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b25eb4e2-3da2-4935-97cf-bf1dda8b9930 · outbound

This paper cites LLM cyber evaluations don’t capture real-world risk,.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting LLM cyber evaluations don’t capture real-world risk,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd764713-2be5-4852-b2a9-32a041fabb52 · outbound

This paper cites LLM Cyber Evaluations Don't Capture Real-World Risk.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting LLM Cyber Evaluations Don't Capture Real-World Risk

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-07-03T02:07:33.283723Z

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 e7404c52-2c9e-4187-8065-84534df7ec12 · outbound

This paper cites Data contamination: From memorization to exploitation.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Data contamination: From memorization to exploitation

Reference 70

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6790727a-79ba-4a09-953a-7d2c552da715 · outbound

This paper cites Building less-flawed metrics: Understanding and creating better measurement and incentive systems.Patterns, 2023.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Building less-flawed metrics: Understanding and creating better measurement and incentive systems.Patterns, 2023

Reference 71

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3aae7fab-7a26-4247-9fd2-fda3ad46b397 · outbound

This paper cites Oard, Luca Soldaini, Ian Soboroff, Orion Weller, Efsun Kayi, Kate Sanders, Marc Mason, and Noah Hibbler.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Oard, Luca Soldaini, Ian Soboroff, Orion Weller, Efsun Kayi, Kate Sanders, Marc Mason, and Noah Hibbler

Reference 72

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f0053b5-d011-4038-8aaf-224e8c215a18 · outbound

This paper cites STREAM (ChemBio): A Standard for Transparently Reporting Evaluations in AI Model Reports.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting STREAM (ChemBio): A Standard for Transparently Reporting Evaluations in AI Model Reports

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-07-03T02:07:33.277542Z

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 e96cffc0-b2b3-4478-b5be-ecdd3c63ba0c · outbound

This paper cites Adding error bars to evals: A statistical approach to language model evaluations,.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Adding error bars to evals: A statistical approach to language model evaluations,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74551f00-3c90-46f7-ba22-fef4cc9354a3 · outbound

This paper cites Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations

Reference 75

Resolution: verified exact; arxiv_id, observed 2026-07-03T02:07:33.250825Z

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 aa00e479-e585-4cb3-a540-baa559133031 · outbound

This paper cites Model cards for model reporting.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Model cards for model reporting

Reference 76

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d68ccd6-9171-44d5-b05f-9d322032e90c · outbound

This paper cites State of What Art? A Call for Multi-Prompt LLM Evaluation.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting State of What Art? A Call for Multi-Prompt LLM Evaluation

Reference 77

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T02:07:33.257770Z

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 ba2db502-f4e6-4dc8-9bb7-9b329e9e870b · outbound

This paper cites Extrinsic evaluation of machine translation metrics.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Extrinsic evaluation of machine translation metrics

Reference 78

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4d3c5ae-5318-4bc0-bbe8-66ad1d569c5b · outbound

This paper cites an unresolved cited work.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Unresolved cited work

Reference 79

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4546810-6750-4ab7-ab34-ec27b1103b63 · outbound

This paper cites A Survey on Large Language Model Benchmarks.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting A Survey on Large Language Model Benchmarks

Reference 80

Resolution: verified exact; arxiv_id, observed 2026-07-03T01:57:32.617219Z

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 f5e1abd3-f75f-4508-bd04-b077584b989f · outbound

This paper cites Evaluation of DeepSeek AI models.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Evaluation of DeepSeek AI models

Reference 81

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eda49157-95e1-4994-ac0a-88e683ced263 · outbound

This paper cites an unresolved cited work.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Unresolved cited work

Reference 82

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 447529a4-fd12-4e37-8abf-2c6f998706cb · outbound

This paper cites Byun, Kevin Wei, and Toby Webster.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Byun, Kevin Wei, and Toby Webster

Reference 83

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf33c5a0-7ccf-490a-9ae6-53808fd41cfb · outbound

This paper cites an unresolved cited work.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Unresolved cited work

Reference 84

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8ddff62-d616-403d-9d3b-f918f010c1ca · outbound

This paper cites Toward best practices for AI evaluation and governance: A proposal for a european union general-purpose AI model evaluation standards task force.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Toward best practices for AI evaluation and governance: A proposal for a european union general-purpose AI model evaluation standards task force

Reference 85

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81a91e57-c3b8-4afd-ba90-afd8f2d8b6eb · outbound

This paper cites Data cards: Purposeful and transparent dataset documentation for responsible ai.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Data cards: Purposeful and transparent dataset documentation for responsible ai

Reference 86

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34d37655-022d-4497-b11e-0505caafc797 · outbound

This paper cites The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harms.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harms

Reference 87

Resolution: verified exact; arxiv_id, observed 2026-07-03T02:07:33.246972Z

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 34f6a5ad-d167-4e2d-b9d4-a22bf6d56317 · outbound

This paper cites Mellor, Jonathan Uesato, Po-Sen Huang, Johannes Welbl, Laura Weidinger, Sumanth Dathathri, Amelia Glaese, Geoffrey Irving, Iason Gabriel, William Isaac, and Lisa Anne Hendricks.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Mellor, Jonathan Uesato, Po-Sen Huang, Johannes Welbl, Laura Weidinger, Sumanth Dathathri, Amelia Glaese, Geoffrey Irving, Iason Gabriel, William Isaac, and Lisa Anne Hendricks

Reference 88

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34045775-d44e-43ae-bedc-d26990cc8319 · outbound

This paper cites Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?

Reference 89

Resolution: verified exact; arxiv_id, observed 2026-07-03T02:07:33.268541Z

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 2327c442-bf51-4758-a333-70bcceac9caf · outbound

This paper cites Kochenderfer.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Kochenderfer

Reference 90

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76033481-4fb4-41c0-90c1-3edb3c4d1ca4 · outbound

This paper cites Measur- ing what matters: Connecting ai ethics evaluations to system attributes, hazards, and harms.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Measur- ing what matters: Connecting ai ethics evaluations to system attributes, hazards, and harms

Reference 91

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e89ba13-7217-408b-81c6-8bc2f39aeee6 · outbound

This paper cites Measurement to meaning: A validity-centered framework for AI evaluation, 2025.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Measurement to meaning: A validity-centered framework for AI evaluation, 2025

Reference 92

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78190728-5411-439c-a543-619cec0a4454 · outbound

This paper cites an unresolved cited work.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Unresolved cited work

Reference 93

Resolution: malformed identifier; doi_truncated, observed 2026-06-27T16:21:02.273864Z

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 241f9a46-824b-4056-b059-6e49503cc97a · outbound

This paper cites an unresolved cited work.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Unresolved cited work

Reference 94

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e2dd374-9a2f-4173-96dd-0a61d0dfe4e9 · outbound

This paper cites Reality check: A new evaluation ecosystem is necessary to understand ai’s real world effects, 2025.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Reality check: A new evaluation ecosystem is necessary to understand ai’s real world effects, 2025

Reference 95

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6616e616-cfbf-45a5-802e-8df446a2038d · outbound

This paper cites Improving methodologies for agentic evaluations across domains: Leakage of sensitive information, fraud and cybersecurity threats, 2026.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Improving methodologies for agentic evaluations across domains: Leakage of sensitive information, fraud and cybersecurity threats, 2026

Reference 96

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2aad5fe4-fc00-42c9-9231-205c1d2855b6 · outbound

This paper cites Model evaluation for extreme risks.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Model evaluation for extreme risks

Reference 97

Resolution: verified exact; arxiv_id, observed 2026-07-03T02:07:33.287703Z

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 55f2fff8-51cc-4ae6-958c-cbabd9dabc08 · outbound

This paper cites Smith, Beyza Ermis, Marzieh Fadaee, and Sara Hooker.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Smith, Beyza Ermis, Marzieh Fadaee, and Sara Hooker

Reference 98

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02c5708e-1869-4364-9c27-a627fa4201ce · outbound

This paper cites Daly, Michael Hind, David Piorkowski, Xiangliang Zhang, Nuno Moniz, and Nitesh V Chawla.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Daly, Michael Hind, David Piorkowski, Xiangliang Zhang, Nuno Moniz, and Nitesh V Chawla

Reference 99

Resolution: unresolved; no resolver link, observed 2026-06-27T16:11:36.483820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83d0a000-3cbf-4c26-9c86-6e7225b07dc3 · outbound

This paper cites Verifiable evaluations of machine learning models using zkSNARKs.

Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting Verifiable evaluations of machine learning models using zkSNARKs

Reference 100

Resolution: verified exact; arxiv_id, observed 2026-07-03T01:57:32.614700Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers