Paper Citation Record · LEDGER

AI Benchmarks and Datasets for LLM Evaluation

As of 22 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2412.01020.

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

pith.paper-citation-record.v1
2412.01020 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:49:34.499531Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T04:07:56.287352Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:28:44.552688Z

Reference resolution

54 of 54 outbound references displayed

verified exact0
verified fuzzy35
unresolved19
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 959064f1-1cef-4252-906d-e03d12c2d360 · outbound

This paper cites https://aisafetybulgaria.c om/.

AI Benchmarks and Datasets for LLM Evaluation https://aisafetybulgaria.c om/

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.494189Z

Source-reported events for the cited work

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Observation 768576e5-db86-4bd5-b740-1828ab195762 · outbound

This paper cites Tpcx-ai - an industry standard benc hmark for artiﬁcial intelligence and machine learning systems.

AI Benchmarks and Datasets for LLM Evaluation Tpcx-ai - an industry standard benc hmark for artiﬁcial intelligence and machine learning systems

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.481294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 595bf9b7-49cb-428f-8c43-23511f8d17b0 · outbound

This paper cites https://compl-ai.org/.

AI Benchmarks and Datasets for LLM Evaluation https://compl-ai.org/

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.468167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b852ae81-e737-4721-831a-9bfe82e099b8 · outbound

This paper cites Relevai-reviewer: A benchmark on AI reviewers for survey paper relevance.

AI Benchmarks and Datasets for LLM Evaluation Relevai-reviewer: A benchmark on AI reviewers for survey paper relevance

Reference 4

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Source-reported events for the cited work

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Observation c5a99daa-ed8a-4c81-b982-79c205a95d25 · outbound

This paper cites Robustbench: a standardized adversarial ro bustness bench- mark.

AI Benchmarks and Datasets for LLM Evaluation Robustbench: a standardized adversarial ro bustness bench- mark

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.455802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 75850ed8-80ce-4434-8daf-38b73a9c410e · outbound

This paper cites https://artiﬁcialintelligenceact.eu /the-act/.

AI Benchmarks and Datasets for LLM Evaluation https://artiﬁcialintelligenceact.eu /the-act/

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.442610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b267cce2-1bd9-4cfc-9b5d-b9ab5095e816 · outbound

This paper cites https://di gital- strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai.

AI Benchmarks and Datasets for LLM Evaluation https://di gital- strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.429455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1232386a-9562-4f1a-a896-34461e041741 · outbound

This paper cites Compl-ai framework: A technical interpretation and llm benchmarkin g suite for the eu artiﬁcial intelligence act, 2024.

AI Benchmarks and Datasets for LLM Evaluation Compl-ai framework: A technical interpretation and llm benchmarkin g suite for the eu artiﬁcial intelligence act, 2024

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.416377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ba014203-b1b9-423d-a334-fd5ee4d2adb7 · outbound

This paper cites Measuring massive multita sk language understanding.

AI Benchmarks and Datasets for LLM Evaluation Measuring massive multita sk language understanding

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.401262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5d3b5543-7ead-435d-8400-7e2da9421296 · outbound

This paper cites Measuri ng mathe- matical problem solving with the MATH dataset.

AI Benchmarks and Datasets for LLM Evaluation Measuri ng mathe- matical problem solving with the MATH dataset

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.386821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8dcf9c9a-e335-440e-92ff-49c4d3a6b235 · outbound

This paper cites Weld, and Luke Zett lemoyer.

AI Benchmarks and Datasets for LLM Evaluation Weld, and Luke Zett lemoyer

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.373656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 31d31134-2521-42bc-890c-fcb31d6d2140 · outbound

This paper cites OpenAssistant Conversations -- Democratizing Large Language Model Alignment.

AI Benchmarks and Datasets for LLM Evaluation OpenAssistant Conversations -- Democratizing Large Language Model Alignment

Reference 12

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Source-reported events for the cited work

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Observation 10e6adef-20f7-49ad-8eda-32d82c7a0935 · outbound

This paper cites Back- doorllm: A comprehensive benchmark for backdoor attacks on large lan- guage models, 2024.

AI Benchmarks and Datasets for LLM Evaluation Back- doorllm: A comprehensive benchmark for backdoor attacks on large lan- guage models, 2024

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.359667Z

Source-reported events for the cited work

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Observation a3ab6eb7-bfbb-4464-ae23-cf7c389c373f · outbound

This paper cites an unresolved cited work.

AI Benchmarks and Datasets for LLM Evaluation Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-12T04:49:35.346655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b024e696-d46b-48e5-baa3-d74efc343611 · outbound

This paper cites GLoRE: Evaluating Logical Reasoning of Large Language Models.

AI Benchmarks and Datasets for LLM Evaluation GLoRE: Evaluating Logical Reasoning of Large Language Models

Reference 15

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Source-reported events for the cited work

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Observation f9509a7b-ff82-44b9-8da8-450226793d40 · outbound

This paper cites Metabox: A benchmark plat- form for meta-black-box optimization with reinforcement l earning.

AI Benchmarks and Datasets for LLM Evaluation Metabox: A benchmark plat- form for meta-black-box optimization with reinforcement l earning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.333475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4ab148b2-0aa1-4d3d-b87b-0a3b5c1f0b6e · outbound

This paper cites Ok-vqa: A visual question answering benchmark requi ring external knowledge, 2019.

AI Benchmarks and Datasets for LLM Evaluation Ok-vqa: A visual question answering benchmark requi ring external knowledge, 2019

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.321026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0fd44be2-0d82-4afa-b5ea-4905c02a7fc3 · outbound

This paper cites Abstractive text summarization u sing sequence- to-sequence rnns and beyond.

AI Benchmarks and Datasets for LLM Evaluation Abstractive text summarization u sing sequence- to-sequence rnns and beyond

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.308111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 73b65f13-8f29-4de2-b2bf-a0fc493bcc68 · outbound

This paper cites Adversarial NLI: A new benchmark for natura l language understanding.

AI Benchmarks and Datasets for LLM Evaluation Adversarial NLI: A new benchmark for natura l language understanding

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.293849Z

Source-reported events for the cited work

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Observation caa6d431-23da-4cb6-a193-00024761feeb · outbound

This paper cites https://oecd.ai/en/ai-pr inciples.

AI Benchmarks and Datasets for LLM Evaluation https://oecd.ai/en/ai-pr inciples

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.280799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a9363cd5-ffaf-4dcd-91b5-f9b2e87961e3 · outbound

This paper cites The LAMBADA dataset: Word prediction requir- ing a broad discourse context.

AI Benchmarks and Datasets for LLM Evaluation The LAMBADA dataset: Word prediction requir- ing a broad discourse context

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.266378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation de3aa830-3499-4d37-b28c-7f1490d7eda4 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

AI Benchmarks and Datasets for LLM Evaluation GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 22

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Source-reported events for the cited work

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Observation 32cc2676-71cc-441d-a613-b2dc3c66fdad · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at sc ale.

AI Benchmarks and Datasets for LLM Evaluation Winogrande: An adversarial winograd schema challenge at sc ale

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.251863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6348edc0-763c-4d98-981e-065481892f0a · outbound

This paper cites Hadﬁel d, Richard Ngo, Konstantin Pilz, George Gor, Emma Bluemke, Sarah Shoker, Ja net Egan, Robert F.

AI Benchmarks and Datasets for LLM Evaluation Hadﬁel d, Richard Ngo, Konstantin Pilz, George Gor, Emma Bluemke, Sarah Shoker, Ja net Egan, Robert F

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.239019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 141e0544-db56-4537-8ad7-db555dc02946 · outbound

This paper cites Sur- vey of diﬀerent large language model architectures: Trends , benchmarks, and challenges.

AI Benchmarks and Datasets for LLM Evaluation Sur- vey of diﬀerent large language model architectures: Trends , benchmarks, and challenges

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.224479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5c0eef8c-bb5a-4fe3-8961-57df44b03318 · outbound

This paper cites Concep tnet 5.5: An open multilingual graph of general knowledge.

AI Benchmarks and Datasets for LLM Evaluation Concep tnet 5.5: An open multilingual graph of general knowledge

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.211305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8e2cb9b3-8344-4777-9005-28c997947f30 · outbound

This paper cites Musr: Testing the limits of chain-of-thought with multiste p soft reason- ing.

AI Benchmarks and Datasets for LLM Evaluation Musr: Testing the limits of chain-of-thought with multiste p soft reason- ing

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.198454Z

Source-reported events for the cited work

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Observation a20935e0-0fbc-4411-88fd-e8cd5c050af2 · outbound

This paper cites Conﬂictbank: A benchmark for evaluating the inﬂuence of knowledge conﬂicts in llm, 2024.

AI Benchmarks and Datasets for LLM Evaluation Conﬂictbank: A benchmark for evaluating the inﬂuence of knowledge conﬂicts in llm, 2024

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.185345Z

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Observation cf6d6eb4-7de6-4ec0-b3e1-a900a43a10d8 · outbound

This paper cites A corpus for reasoning about natural language gr ounded in photographs, 2019.

AI Benchmarks and Datasets for LLM Evaluation A corpus for reasoning about natural language gr ounded in photographs, 2019

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.172027Z

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Observation a87cdc31-c0d4-4d97-9ffc-8a2595dc2b75 · outbound

This paper cites Table meets llm: Can large language models understand struc tured table data? a benchmark and empirical study, 2024.

AI Benchmarks and Datasets for LLM Evaluation Table meets llm: Can large language models understand struc tured table data? a benchmark and empirical study, 2024

Reference 30

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Observation 436f95fd-5862-4c93-964f-41ceb378d71f · outbound

This paper cites Le, Ed H.

AI Benchmarks and Datasets for LLM Evaluation Le, Ed H

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.142715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5870f8e5-7fd8-4410-af03-e0a4673eb9d5 · outbound

This paper cites Commonsenseqa: A question answering challenge targeting c ommonsense knowledge.

AI Benchmarks and Datasets for LLM Evaluation Commonsenseqa: A question answering challenge targeting c ommonsense knowledge

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.128940Z

Source-reported events for the cited work

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Observation da8f0378-5315-42b6-87ca-298895d91458 · outbound

This paper cites Ltlbench: Towards bench marks for evaluating temporal logic reasoning in large language mode ls.

AI Benchmarks and Datasets for LLM Evaluation Ltlbench: Towards bench marks for evaluating temporal logic reasoning in large language mode ls

Reference 33

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Observation 954456a9-cbf4-4331-b187-94545c406c76 · outbound

This paper cites Kirkpatrick, Feiyi Wang, Tom Gibbs, Venkatram Vishwanath, Mallikarjun Shankar, Geoﬀrey C.

AI Benchmarks and Datasets for LLM Evaluation Kirkpatrick, Feiyi Wang, Tom Gibbs, Venkatram Vishwanath, Mallikarjun Shankar, Geoﬀrey C

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.115471Z

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Observation 25f27a24-2548-4309-b237-0cff8158c062 · outbound

This paper cites Madai, Emilie Wiinb lad Mathez, 25 Jesmin Jahan Tithi, Magnus Westerlund, Renee Wurth, and Rob erto V.

AI Benchmarks and Datasets for LLM Evaluation Madai, Emilie Wiinb lad Mathez, 25 Jesmin Jahan Tithi, Magnus Westerlund, Renee Wurth, and Rob erto V

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.102325Z

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Observation 64a38680-7320-44cf-b4b3-208c97dd72fa · outbound

This paper cites Introducing v0.5 of the AI Safety Benchmark from MLCommons.

AI Benchmarks and Datasets for LLM Evaluation Introducing v0.5 of the AI Safety Benchmark from MLCommons

Reference 36

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AI Benchmarks and Datasets for LLM Evaluation Unresolved cited work

Reference 37

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Observation dd9703b6-3513-4e6c-b93a-19a841bcb088 · outbound

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AI Benchmarks and Datasets for LLM Evaluation Unresolved cited work

Reference 38

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Observation 9694d149-2b58-40a5-b696-3d6f82c39ce7 · outbound

This paper cites CORD-19: The COVID-19 Open Research Dataset.

AI Benchmarks and Datasets for LLM Evaluation CORD-19: The COVID-19 Open Research Dataset

Reference 39

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Observation 12ca922d-2dcb-43e9-8888-3c4b06c828be · outbound

This paper cites MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark.

AI Benchmarks and Datasets for LLM Evaluation MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 40

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Source-reported events for the cited work

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Observation 758a8961-3d6a-4944-b487-63ddebaff14b · outbound

This paper cites CausalBench: A comprehensive benchmark for evaluating causal reasoning capabilities of large language models.

AI Benchmarks and Datasets for LLM Evaluation CausalBench: A comprehensive benchmark for evaluating causal reasoning capabilities of large language models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.061640Z

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Observation 8c69c960-5975-41e9-98fb-689d2e8a499f · outbound

This paper cites LogicVista: Multimodal LLM Logical Reasoning Benchmark in Visual Contexts.

AI Benchmarks and Datasets for LLM Evaluation LogicVista: Multimodal LLM Logical Reasoning Benchmark in Visual Contexts

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T04:49:34.448945Z

Source-reported events for the cited work

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Observation 8a57a991-f3da-4a06-8e1a-a1c0b5aecbbe · outbound

This paper cites Quick and (not so) dirty: Unsupervised selection of justiﬁcation sentences f or multi-hop ques- tion answering.

AI Benchmarks and Datasets for LLM Evaluation Quick and (not so) dirty: Unsupervised selection of justiﬁcation sentences f or multi-hop ques- tion answering

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.047700Z

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Observation 5e7dfec3-743e-430c-9517-9d9a8d40c46b · outbound

This paper cites Eval- uating the quality of hallucination benchmarks for large vi sion-language models.

AI Benchmarks and Datasets for LLM Evaluation Eval- uating the quality of hallucination benchmarks for large vi sion-language models

Reference 44

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Source-reported events for the cited work

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Observation 20f81d44-da0d-41b9-ab53-27ec1ca13751 · outbound

This paper cites https://z-inspection.org/.

AI Benchmarks and Datasets for LLM Evaluation https://z-inspection.org/

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:35.032335Z

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Observation 42407999-af0d-4843-9c5c-43c813337483 · outbound

This paper cites an unresolved cited work.

AI Benchmarks and Datasets for LLM Evaluation Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-12T04:49:35.018580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8cc604dd-0a84-4bfb-ac5e-869ec535b2d4 · outbound

This paper cites Hellaswag: Can a machine really ﬁnish your sentence? In Anna Korho- nen, David R.

AI Benchmarks and Datasets for LLM Evaluation Hellaswag: Can a machine really ﬁnish your sentence? In Anna Korho- nen, David R

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:34.990279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 79e80c6a-d471-4375-b5de-162f8947408f · outbound

This paper cites MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models.

AI Benchmarks and Datasets for LLM Evaluation MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T04:49:34.478030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f429688-6ded-4117-9a01-d6e88f1f15cd · outbound

This paper cites Reef- knot: A comprehensive benchmark for relation hallucinatio n evaluation, analysis and mitigation in multimodal large language model s, 2024.

AI Benchmarks and Datasets for LLM Evaluation Reef- knot: A comprehensive benchmark for relation hallucinatio n evaluation, analysis and mitigation in multimodal large language model s, 2024

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:34.976392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bb8602bc-0507-49ad-b393-0c6d3a0fdaff · outbound

This paper cites Revolutionizing data base q&a with large language models: Comprehensive benchmark and evalua tion, 2024.

AI Benchmarks and Datasets for LLM Evaluation Revolutionizing data base q&a with large language models: Comprehensive benchmark and evalua tion, 2024

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:49:34.961429Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2207480c-5c0f-4a4c-9051-22aba14151ab · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

AI Benchmarks and Datasets for LLM Evaluation Instruction-Following Evaluation for Large Language Models

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T04:49:34.490762Z

Source-reported events for the cited work

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Observation 56dd0cbf-1cce-40a4-965c-225238405a70 · outbound

This paper cites CausalBench: A Comprehensive Benchmark for Causal Learning Capability of LLMs.

AI Benchmarks and Datasets for LLM Evaluation CausalBench: A Comprehensive Benchmark for Causal Learning Capability of LLMs

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-12T04:49:34.495253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf60c4e1-4baf-46d8-b430-995295a8caff · outbound

This paper cites an unresolved cited work.

AI Benchmarks and Datasets for LLM Evaluation Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-12T04:49:34.946685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3c1835ad-51b0-4747-97f1-e0064a538d51 · outbound

This paper cites an unresolved cited work.

AI Benchmarks and Datasets for LLM Evaluation Unresolved cited work

Reference 2024

Resolution: unresolved; raw_fallback, observed 2026-08-12T04:49:35.004190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation ddab16a9-f941-4960-9d2b-7ecee0ac701b · inbound

Formalizing and Mitigating Structural Distortion in LLM Attention for Graph Reasoning cites this paper.

Formalizing and Mitigating Structural Distortion in LLM Attention for Graph Reasoning AI Benchmarks and Datasets for LLM Evaluation

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:28:44.554094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.