A Conceptual Framework for AI Capability Evaluations | Paper Citation Record

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This paper cites Early insights from developing question-answer evaluations for frontier AI , 2024.

A Conceptual Framework for AI Capability Evaluations Early insights from developing question-answer evaluations for frontier AI , 2024

Reference 1

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This paper cites Benchmarking foundation models with language-model-as-an-examiner.

A Conceptual Framework for AI Capability Evaluations Benchmarking foundation models with language-model-as-an-examiner

Reference 2

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This paper cites Declare and Justify: Explicit assumptions in AI evaluations are necessary for effective regulation.

A Conceptual Framework for AI Capability Evaluations Declare and Justify: Explicit assumptions in AI evaluations are necessary for effective regulation

Reference 3

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This paper cites A quantitative study of nlp approaches to question difficulty estimation.

A Conceptual Framework for AI Capability Evaluations A quantitative study of nlp approaches to question difficulty estimation

Reference 4

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This paper cites Evaluating AI for Law: Bridging the Gap with Open-Source Solutions.

A Conceptual Framework for AI Capability Evaluations Evaluating AI for Law: Bridging the Gap with Open-Source Solutions

Reference 5

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This paper cites F., Ammanamanchi, P.

A Conceptual Framework for AI Capability Evaluations F., Ammanamanchi, P

Reference 6

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A Conceptual Framework for AI Capability Evaluations R., Steunebrink, B

Reference 7

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This paper cites T., Li, Y., Lundberg, S., et al.

A Conceptual Framework for AI Capability Evaluations T., Li, Y., Lundberg, S., et al

Reference 8

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This paper cites Evaluating AI Evaluation: Perils and Prospects.

A Conceptual Framework for AI Capability Evaluations Evaluating AI Evaluation: Perils and Prospects

Reference 9

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This paper cites Paradigms of AI Evaluation: Mapping Goals, Methodologies and Culture.

A Conceptual Framework for AI Capability Evaluations Paradigms of AI Evaluation: Mapping Goals, Methodologies and Culture

Reference 10

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This paper cites Code Benchmarks Should Prioritize Rigor, Reliability, and Reproducibility.

A Conceptual Framework for AI Capability Evaluations Code Benchmarks Should Prioritize Rigor, Reliability, and Reproducibility

Reference 11

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This paper cites L., Bucknall, B., Haupt, A., Wei, K., Scheurer, J., Hobbhahn, M., et al.

A Conceptual Framework for AI Capability Evaluations L., Bucknall, B., Haupt, A., Wei, K., Scheurer, J., Hobbhahn, M., et al

Reference 12

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This paper cites Leveraging the Context through Multi-Round Interactions for Jailbreaking Attacks.

A Conceptual Framework for AI Capability Evaluations Leveraging the Context through Multi-Round Interactions for Jailbreaking Attacks

Reference 13

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This paper cites N., Li, T., Li, D., Zhu, B., Zhang, H., Jordan, M., Gonzalez, J.

A Conceptual Framework for AI Capability Evaluations N., Li, T., Li, D., Zhu, B., Zhang, H., Jordan, M., Gonzalez, J

Reference 14

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This paper cites On the limitations of reference-free evaluations of generated text.

A Conceptual Framework for AI Capability Evaluations On the limitations of reference-free evaluations of generated text

Reference 15

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This paper cites R., Guo, S., Valko, M., Lillicrap, T., Jimenez Rezende, D., Bengio, Y., Mozer, M.

A Conceptual Framework for AI Capability Evaluations R., Guo, S., Valko, M., Lillicrap, T., Jimenez Rezende, D., Bengio, Y., Mozer, M

Reference 16

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This paper cites Generalization or memorization: Data contamination and trustworthy evaluation for large language models.

A Conceptual Framework for AI Capability Evaluations Generalization or memorization: Data contamination and trustworthy evaluation for large language models

Reference 17

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This paper cites W., Barocas, S., Atalla, C., Chouldechova, A., and Wallach, H.

A Conceptual Framework for AI Capability Evaluations W., Barocas, S., Atalla, C., Chouldechova, A., and Wallach, H

Reference 18

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This paper cites Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation.

A Conceptual Framework for AI Capability Evaluations Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation

Reference 19

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This paper cites Second draft of the general purpose AI code of practice, April 2024.

A Conceptual Framework for AI Capability Evaluations Second draft of the general purpose AI code of practice, April 2024

Reference 20

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Observation b1fe028a-aa7d-46f4-9a9f-035d54b4ab7a · outbound

This paper cites Issue brief: Early best practices for frontier AI safety evaluations, 2024.

A Conceptual Framework for AI Capability Evaluations Issue brief: Early best practices for frontier AI safety evaluations, 2024

Reference 21

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This paper cites Llm-based nlg evaluation: Current status and challenges.

A Conceptual Framework for AI Capability Evaluations Llm-based nlg evaluation: Current status and challenges

Reference 22

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A Conceptual Framework for AI Capability Evaluations A case for better evaluation standards in nlg

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This paper cites Repairing the cracked foundation: A survey of obstacles in evaluation practices for generated text.

A Conceptual Framework for AI Capability Evaluations Repairing the cracked foundation: A survey of obstacles in evaluation practices for generated text

Reference 24

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This paper cites Legalbench: A collaboratively built benchmark for measuring legal reasoning in large language models.

A Conceptual Framework for AI Capability Evaluations Legalbench: A collaboratively built benchmark for measuring legal reasoning in large language models

Reference 25

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A Conceptual Framework for AI Capability Evaluations R., Hullman, J., and Subramonyam, H

Reference 26

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A Conceptual Framework for AI Capability Evaluations Deception abilities emerged in large language models

Reference 27

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This paper cites Machine Psychology.

A Conceptual Framework for AI Capability Evaluations Machine Psychology

Reference 28

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A Conceptual Framework for AI Capability Evaluations a m \"a l \

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A Conceptual Framework for AI Capability Evaluations and Sharadin, N

Reference 30

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A Conceptual Framework for AI Capability Evaluations Unresolved cited work

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A Conceptual Framework for AI Capability Evaluations R., Srivastava, A., and Agrawal, P

Reference 32

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A Conceptual Framework for AI Capability Evaluations Unveiling LLM Evaluation Focused on Metrics: Challenges and Solutions

Reference 33

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A Conceptual Framework for AI Capability Evaluations An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4

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A Conceptual Framework for AI Capability Evaluations M ath P rompter: Mathematical reasoning using large language models

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A Conceptual Framework for AI Capability Evaluations Reference-free Evaluation Metrics for Text Generation: A Survey

Reference 36

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A Conceptual Framework for AI Capability Evaluations Toward best research practices in AI Psychology

Reference 37

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A Conceptual Framework for AI Capability Evaluations Stop uploading test data in plain text: Practical strategies for mitigating data contamination by evaluation benchmarks

Reference 38

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A Conceptual Framework for AI Capability Evaluations Cladder: assessing causal reasoning in language models

Reference 39

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A Conceptual Framework for AI Capability Evaluations T., and Sch \"o lkopf, B

Reference 40

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A Conceptual Framework for AI Capability Evaluations R., Rockt \"a schel, T., and Perez, E

Reference 41

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A Conceptual Framework for AI Capability Evaluations Causal reasoning and large language models: Opening a new frontier for causality

Reference 42

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A Conceptual Framework for AI Capability Evaluations AI Agent Governance: A Field Guide

Reference 43

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A Conceptual Framework for AI Capability Evaluations Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation

Reference 44

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A Conceptual Framework for AI Capability Evaluations P., Wu, H., and Yu, H

Reference 45

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A Conceptual Framework for AI Capability Evaluations Unresolved cited work

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A Conceptual Framework for AI Capability Evaluations J., Kawaguchi, K., Gidel, G., Bengio, Y., Malkin, N., and Jain, M

Reference 47

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A Conceptual Framework for AI Capability Evaluations Leveraging large language models for nlg evaluation: Advances and challenges

Reference 48

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A Conceptual Framework for AI Capability Evaluations D., Re, C., Acosta-Navas, D., Hudson, D

Reference 49

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A Conceptual Framework for AI Capability Evaluations Rethinking Model Evaluation as Narrowing the Socio-Technical Gap

Reference 50

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A Conceptual Framework for AI Capability Evaluations D., and Schmidt, L

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This paper cites Against the achilles' heel: A survey on red teaming for generative models.

A Conceptual Framework for AI Capability Evaluations Against the achilles' heel: A survey on red teaming for generative models

Reference 52

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A Conceptual Framework for AI Capability Evaluations Datasets for Large Language Models: A Comprehensive Survey

Reference 53

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A Conceptual Framework for AI Capability Evaluations Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence

Reference 54

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A Conceptual Framework for AI Capability Evaluations Ablation Studies in Artificial Neural Networks

Reference 55

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A Conceptual Framework for AI Capability Evaluations Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations

Reference 56

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A Conceptual Framework for AI Capability Evaluations Auditing large language models: a three-layered approach

Reference 57

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A Conceptual Framework for AI Capability Evaluations Evaluating the performance of large language models via debates

Reference 58

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This paper cites and Kapoor, S.

A Conceptual Framework for AI Capability Evaluations and Kapoor, S

Reference 59

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A Conceptual Framework for AI Capability Evaluations Oecd framework for the classification of ai systems

Reference 60

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A Conceptual Framework for AI Capability Evaluations and Kang, E

Reference 61

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A Conceptual Framework for AI Capability Evaluations Llm evaluators recognize and favor their own generations

Reference 62

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A Conceptual Framework for AI Capability Evaluations T., and Soder, L

Reference 63

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This paper cites Preliminary suggestions for rigorous gpai model evaluations.

A Conceptual Framework for AI Capability Evaluations Preliminary suggestions for rigorous gpai model evaluations

Reference 64

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A Conceptual Framework for AI Capability Evaluations Discovering language model behaviors with model-written evaluations

Reference 65

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A Conceptual Framework for AI Capability Evaluations Understanding and Benchmarking Artificial Intelligence: OpenAI's o3 Is Not AGI

Reference 66

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A Conceptual Framework for AI Capability Evaluations The roots search tool: Data transparency for llms

Reference 67

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A Conceptual Framework for AI Capability Evaluations D., Denton, E., Bender, E

Reference 68

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This paper cites Large language model evaluation via multi ai agents: Preliminary results.

A Conceptual Framework for AI Capability Evaluations Large language model evaluation via multi ai agents: Preliminary results

Reference 69

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A Conceptual Framework for AI Capability Evaluations A., Comanescu, R., Akbulut, C., Stepleton, T., Mateos-Garcia, J., Bergman, S., Kay, J., et al

Reference 70

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This paper cites Betterbench: Assessing AI benchmarks, uncovering issues, and establishing best practices.

A Conceptual Framework for AI Capability Evaluations Betterbench: Assessing AI benchmarks, uncovering issues, and establishing best practices

Reference 71

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A Conceptual Framework for AI Capability Evaluations Unresolved cited work

Reference 72

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A Conceptual Framework for AI Capability Evaluations Open Problems in Technical AI Governance

Reference 73

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A Conceptual Framework for AI Capability Evaluations Better than random: reliable nlg human evaluation with constrained active sampling

Reference 74

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A Conceptual Framework for AI Capability Evaluations A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Reference 75

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A Conceptual Framework for AI Capability Evaluations L., and Agirre, E

Reference 76

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A Conceptual Framework for AI Capability Evaluations Targeting the benchmark: On methodology in current natural language processing research

Reference 77

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A Conceptual Framework for AI Capability Evaluations The Prompt Report: A Systematic Survey of Prompt Engineering Techniques

Reference 78

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A Conceptual Framework for AI Capability Evaluations Quantifying language models' sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:25:57.223251Z

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A Conceptual Framework for AI Capability Evaluations Model evaluation for extreme risks

Reference 80

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A Conceptual Framework for AI Capability Evaluations CHOPS : CH at with customer profile systems for customer service with LLM s

Reference 81

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A Conceptual Framework for AI Capability Evaluations MultiChallenge: A Realistic Multi-Turn Conversation Evaluation Benchmark Challenging to Frontier LLMs

Reference 82

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A Conceptual Framework for AI Capability Evaluations K., Grundy, E

Reference 83

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A Conceptual Framework for AI Capability Evaluations A study of translation edit rate with targeted human annotation

Reference 84

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A Conceptual Framework for AI Capability Evaluations Audit Cards: Contextualizing AI Evaluations

Reference 85

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A Conceptual Framework for AI Capability Evaluations Comprehensive Reassessment of Large-Scale Evaluation Outcomes in LLMs: A Multifaceted Statistical Approach

Reference 86

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:25:53.477142Z

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A Conceptual Framework for AI Capability Evaluations Measuring data science automation: A survey of evaluation tools for ai assistants and agents

Reference 87

Resolution: verified exact; raw_fallback, observed 2026-08-06T23:25:54.212966Z

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A Conceptual Framework for AI Capability Evaluations Unresolved cited work

Reference 88

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A Conceptual Framework for AI Capability Evaluations Best practices for the human evaluation of automatically generated text

Reference 89

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A Conceptual Framework for AI Capability Evaluations M., Huang, W., Mungra, D., Yuanzhe Pang, R., Phang, J., Liu, H., Cho, K., and Bowman, S

Reference 90

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This paper cites Mint: Evaluating llms in multi-turn interaction with tools and language feedback.

A Conceptual Framework for AI Capability Evaluations Mint: Evaluating llms in multi-turn interaction with tools and language feedback

Reference 91

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Observation 89be56e1-8322-4bbc-b116-dbb0f5f3bae1 · outbound

This paper cites Sociotechnical Safety Evaluation of Generative AI Systems.

A Conceptual Framework for AI Capability Evaluations Sociotechnical Safety Evaluation of Generative AI Systems

Reference 92

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A Conceptual Framework for AI Capability Evaluations Toward an Evaluation Science for Generative AI Systems

Reference 93

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A Conceptual Framework for AI Capability Evaluations An ai system evaluation framework for advancing ai safety: Terminology, taxonomy, lifecycle mapping

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:25:56.134856Z

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This paper cites A critical review of causal inference benchmarks for large language models.

A Conceptual Framework for AI Capability Evaluations A critical review of causal inference benchmarks for large language models

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:25:55.938837Z

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Observation 1360cb66-5ad5-4bb0-8ac5-32e483f7ece0 · outbound

This paper cites Evaluatology: The science and engineering of evaluation.

A Conceptual Framework for AI Capability Evaluations Evaluatology: The science and engineering of evaluation

Reference 96

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A Conceptual Framework for AI Capability Evaluations Language model developers should report train-test overlap

Reference 97

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T23:25:53.854157Z

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A Conceptual Framework for AI Capability Evaluations Q., Shaw, R., Anthis, J

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:25:55.777337Z

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This paper cites Pacost: Paired confidence significance testing for benchmark contamination detection in large language models.

A Conceptual Framework for AI Capability Evaluations Pacost: Paired confidence significance testing for benchmark contamination detection in large language models

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:25:55.639990Z

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Observation 739097ec-7486-4a77-88f7-9f66a3f77e30 · outbound

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A Conceptual Framework for AI Capability Evaluations and Kanayet, F

Reference 100

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