Paper Citation Record · LEDGER

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation

As of 8 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2607.14109.

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

pith.paper-citation-record.v1
2607.14109 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T14:42:46.895939Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T03:19:00.468509Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

verified exact0
verified fuzzy0
unresolved40
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a8dbedc3-b580-44a4-a9ac-7ad770bbecec · outbound

This paper cites Matharena: Evaluating llms on uncontaminated math competitions, February 2025.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Matharena: Evaluating llms on uncontaminated math competitions, February 2025

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:43.688759Z

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Observation 8d12022a-c777-4faa-b2d7-cf6bc25524cd · outbound

This paper cites On the Measure of Intelligence.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation On the Measure of Intelligence

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:43.736195Z

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Observation 417d6c12-b74f-4a4b-a868-83dfde7673f4 · outbound

This paper cites FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:43.792576Z

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Observation 62de648b-6cab-427d-b068-09919e94592c · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:43.868953Z

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Observation c81abba5-3dd0-4b90-8299-695c12f88f6d · outbound

This paper cites The language model evaluation harness, July 2024.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation The language model evaluation harness, July 2024

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:43.942648Z

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Observation 3fa92299-6cb3-4ee8-9aa2-6f6e55ec04e1 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Measuring Massive Multitask Language Understanding

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:44.008058Z

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Observation 07c789f4-03fc-4bae-a2b9-ce5bbcb66dd4 · outbound

This paper cites What disease does this patient have? a large-scale open domain question answering dataset from medical exams.Applied Sciences, 11(14):6421, 2021.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation What disease does this patient have? a large-scale open domain question answering dataset from medical exams.Applied Sciences, 11(14):6421, 2021

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:44.077828Z

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Observation 6a17f4d5-65a3-4b16-8856-0ccc80f9cbde · outbound

This paper cites Dynabench: Rethinking benchmarking in NLP.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Dynabench: Rethinking benchmarking in NLP

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:44.125291Z

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Observation 6da2fbb5-5677-4a57-80d3-639352a73d76 · outbound

This paper cites Large language models are zero-shot reasoners.Advances in Neural Information Processing Systems, 35:22199–22213, 2022.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Large language models are zero-shot reasoners.Advances in Neural Information Processing Systems, 35:22199–22213, 2022

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:44.201129Z

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Observation b665a6cf-375d-462c-b5ac-a24d556037b0 · outbound

This paper cites Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:44.258314Z

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Observation 0f54f492-b7ab-455a-bc1f-b81c96cb9f13 · outbound

This paper cites Prompt repetition improves non-reasoning llms.arXiv preprint arXiv:2512.14982, 2025.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Prompt repetition improves non-reasoning llms.arXiv preprint arXiv:2512.14982, 2025

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:44.341417Z

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Observation 41a00ded-0a6b-4172-bbf1-76b0eeb0c10c · outbound

This paper cites Holistic Evaluation of Language Models.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Holistic Evaluation of Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:44.399738Z

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Observation dd81da62-65e4-44e3-9d57-0260c64f53c7 · outbound

This paper cites Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:44.484533Z

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Observation bbd846b0-2b40-4227-8ff3-1ac792f89aa5 · outbound

This paper cites SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:44.560800Z

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Observation 0c9c93e9-8fc0-4bda-a6c1-b5b050d53f65 · outbound

This paper cites Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:44.631644Z

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Observation bc0dad1d-790a-46e4-9562-e30006943550 · outbound

This paper cites Can a suit of armor conduct electricity? a new dataset for open book question answering.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:44.690538Z

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Observation bbe22d41-b35b-4b96-bfe4-2711edc2c830 · outbound

This paper cites State of what art? a call for multi-prompt LLM evaluation.Transactions of the Association for Computational Linguistics, 12:933–949, 2024.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation State of what art? a call for multi-prompt LLM evaluation.Transactions of the Association for Computational Linguistics, 12:933–949, 2024

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:44.771497Z

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Observation 2b98c736-2e7d-4f91-81f7-65991303bab1 · outbound

This paper cites s1: Simple test-time scaling.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation s1: Simple test-time scaling

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:44.851388Z

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Observation 803145ac-f648-4d8e-b804-397b5d9d61b3 · outbound

This paper cites Large language models sensitivity to the order of options in multiple-choice questions.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Large language models sensitivity to the order of options in multiple-choice questions

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:44.933675Z

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Observation 7a2d1432-7497-4207-8d26-67b65e837ee8 · outbound

This paper cites Smith, and Mike Lewis.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Smith, and Mike Lewis

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:44.989333Z

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Observation 08c60252-8d51-437c-9a79-752328250936 · outbound

This paper cites Qwen3 Technical Report.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Qwen3 Technical Report

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:45.058968Z

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Observation ae5a0261-8753-4e0f-a714-525fe8aca56c · outbound

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

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:45.126365Z

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Observation 4208621a-99dc-462f-ae7b-af01e99f96ee · outbound

This paper cites Leveraging large language models for multiple choice question answering.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Leveraging large language models for multiple choice question answering

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:45.188960Z

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Observation d90898b8-5376-45d0-bd23-cfc1466a0610 · outbound

This paper cites Rogers, Inna Goncearenco, Giuseppe Sarli, Igor Galynker, Denis Peskoff, Marine Carpuat, Jules White, Shyamal Anadkat, Alexander Hoyle, and Philip Resnik.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Rogers, Inna Goncearenco, Giuseppe Sarli, Igor Galynker, Denis Peskoff, Marine Carpuat, Jules White, Shyamal Anadkat, Alexander Hoyle, and Philip Resnik

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:45.271611Z

Source-reported events for the cited work

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Observation 69da3077-c6af-40d9-8c9b-e370aff58097 · outbound

This paper cites Quantifying language models’ sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Quantifying language models’ sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:45.351262Z

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Observation 3c9b5ff5-58b6-40e5-9167-4b6ec5955431 · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:45.427128Z

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Observation 386a2a21-f5f9-4ae0-9201-d03189116cb1 · outbound

This paper cites To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:45.567640Z

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Observation 070c18b0-ffd7-407a-a68b-274a9e4b9031 · outbound

This paper cites Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:45.737278Z

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Observation c24b6286-216d-48e8-a2b9-698aee799855 · outbound

This paper cites On the self-verification limitations of large language models on reasoning and planning tasks.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation On the self-verification limitations of large language models on reasoning and planning tasks

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:45.811194Z

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Observation a086f0a8-0857-4400-b802-8181fd1b6235 · outbound

This paper cites Correctbench: A benchmark of self-correction in llms.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Correctbench: A benchmark of self-correction in llms

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:45.891639Z

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Observation 7869f54f-2d87-4e95-bf18-6242d863dbc3 · outbound

This paper cites Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:45.950957Z

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Observation 5d944d42-32fc-4147-8eba-f70080c55be0 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:46.034794Z

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Observation 7ba8a91c-af99-4941-8964-d5d2feba3625 · outbound

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

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:46.087335Z

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Observation 435a630f-28ab-479a-9174-543f4c4ada6f · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Chain-of-thought prompting elicits reasoning in large language models

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:46.157373Z

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Observation fe702455-6e0e-495a-b1e9-bc7d265db053 · outbound

This paper cites Griffiths, Yuan Cao, and Karthik Narasimhan.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Griffiths, Yuan Cao, and Karthik Narasimhan

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:46.222619Z

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Observation 1a961236-da12-45fd-bd23-b8616d05477b · outbound

This paper cites Chi, and Denny Zhou.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Chi, and Denny Zhou

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:46.344230Z

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Observation 4ef20845-7303-4fc7-b64f-855a71505425 · outbound

This paper cites Generate rather than Retrieve: Large Language Models are Strong Context Generators.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Generate rather than Retrieve: Large Language Models are Strong Context Generators

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:46.446916Z

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Observation dfa6988e-f120-447c-9960-8645380e31a3 · outbound

This paper cites Large language models are not robust multiple choice selectors.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Large language models are not robust multiple choice selectors

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:46.560399Z

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Observation 14d137af-1c5b-4983-ba1d-0cb7b9eb896b · outbound

This paper cites Scaling physical reasoning with the physics dataset, 2025.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Scaling physical reasoning with the physics dataset, 2025

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:46.668559Z

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Observation ab524a00-2979-4d1d-8413-3f01fdfb7fa5 · outbound

This paper cites PromptBench: A Unified Library for Evaluation of Large Language Models.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation PromptBench: A Unified Library for Evaluation of Large Language Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-02T14:42:46.785295Z

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Observation 0f23936f-5148-4702-9339-0d8f554e1d0b · outbound

This paper cites Avg. opt.

Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation Avg. opt

Reference 41

Resolution: malformed identifier; no resolver link, observed 2026-08-02T14:42:46.895939Z

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Pith citing papers

Observation c09d33ea-403f-4738-85b1-ac4435c38fb5 · inbound

Sample More, Reflect Less: Self-Refine and Reflexion Lose to Repeated Sampling at Equal Token Cost, from 1.5B to 7B cites this paper.

Sample More, Reflect Less: Self-Refine and Reflexion Lose to Repeated Sampling at Equal Token Cost, from 1.5B to 7B Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-31T03:19:00.468509Z

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