Paper Citation Record · LEDGER

How Benchmark Prediction from Fewer Data Misses the Mark

As of 20 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 6 inbound Pith citation observations for arXiv:2506.07673.

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

pith.paper-citation-record.v1
2506.07673 v2

Coverage vector

measured 66 of 66 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:34:27.021795Z

measured 72 of 72 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T06:40:23.654993Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:49:46.920258Z

Reference resolution

66 of 66 outbound references displayed

verified exact6
verified fuzzy21
unresolved38
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 26ce0089-b6d4-4d42-b7b8-b406dacf525f · outbound

This paper cites Jordan, and Tijana Zrnic.

How Benchmark Prediction from Fewer Data Misses the Mark Jordan, and Tijana Zrnic

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.846205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 12e946a6-75f0-4505-a47d-68740937a7ac · outbound

This paper cites PPI++: Efficient Prediction-Powered Inference.

How Benchmark Prediction from Fewer Data Misses the Mark PPI++: Efficient Prediction-Powered Inference

Reference 2

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Source-reported events for the cited work

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Observation e8998634-c9ec-4efd-a751-da8bce42a030 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

How Benchmark Prediction from Fewer Data Misses the Mark Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T05:34:26.582523Z

Source-reported events for the cited work

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Observation cb62aaaa-d323-4ab9-b403-cb5c3f267220 · outbound

This paper cites The fifth PASCAL recognizing textual entailment challenge.

How Benchmark Prediction from Fewer Data Misses the Mark The fifth PASCAL recognizing textual entailment challenge

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:34:26.595425Z

Source-reported events for the cited work

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Observation b456a683-73b7-4e2a-9d30-d37b78bf7543 · outbound

This paper cites AutoEval Done Right: Using Synthetic Data for Model Evaluation.

How Benchmark Prediction from Fewer Data Misses the Mark AutoEval Done Right: Using Synthetic Data for Model Evaluation

Reference 5

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Source-reported events for the cited work

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Observation 4be3af6d-5e00-46bc-b5ee-b7b40d24cfd3 · outbound

This paper cites A singular value thresholding algorithm for matrix completion.

How Benchmark Prediction from Fewer Data Misses the Mark A singular value thresholding algorithm for matrix completion

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T05:34:26.629844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0696c877-6cb1-450c-898d-563b3cd76d5b · outbound

This paper cites Humans or LLMs as the Judge? A Study on Judgement Biases.

How Benchmark Prediction from Fewer Data Misses the Mark Humans or LLMs as the Judge? A Study on Judgement Biases

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T05:34:26.647558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4900b0b7-bbd8-482c-b3ee-442914c09442 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

How Benchmark Prediction from Fewer Data Misses the Mark Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T05:34:26.659435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a51dd97-39e8-48af-ba9d-61a528884761 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

How Benchmark Prediction from Fewer Data Misses the Mark Training Verifiers to Solve Math Word Problems

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T05:34:26.728273Z

Source-reported events for the cited work

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Observation aad63229-20d8-44ad-9299-46d04ceaf4c6 · outbound

This paper cites Computing the testing error without a testing set.

How Benchmark Prediction from Fewer Data Misses the Mark Computing the testing error without a testing set

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.823011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b98538e5-9647-4134-87d6-3619b1fe2cbf · outbound

This paper cites The PASCAL recognising textual entailment challenge.

How Benchmark Prediction from Fewer Data Misses the Mark The PASCAL recognising textual entailment challenge

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.813136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d22a3f24-6499-4e4b-86a0-4f7c3537c121 · outbound

This paper cites Are labels always necessary for classifier accuracy evaluation? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15069– 15078, 2021.

How Benchmark Prediction from Fewer Data Misses the Mark Are labels always necessary for classifier accuracy evaluation? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15069– 15078, 2021

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.803019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 41110b6f-0e2e-4556-bd50-59291e0a0e13 · outbound

This paper cites Automatically constructing a corpus of sentential paraphrases.

How Benchmark Prediction from Fewer Data Misses the Mark Automatically constructing a corpus of sentential paraphrases

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.791643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 57d1e637-a851-45ce-ac17-aa6b56ab3582 · outbound

This paper cites Limits to scalable evaluation at the frontier: Llm as judge won’t beat twice the data.

How Benchmark Prediction from Fewer Data Misses the Mark Limits to scalable evaluation at the frontier: Llm as judge won’t beat twice the data

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.779352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dce72101-6bdb-446d-9e38-3fdc7196cc92 · outbound

This paper cites Facility location: concepts, models, algo- rithms and case studies.

How Benchmark Prediction from Fewer Data Misses the Mark Facility location: concepts, models, algo- rithms and case studies

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.768782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3eebed2e-4b99-46e3-95ad-6207737980b7 · outbound

This paper cites Open llm leaderboard v2.

How Benchmark Prediction from Fewer Data Misses the Mark Open llm leaderboard v2

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d1bfc293-0b96-49ef-b95b-6e74709768c6 · outbound

This paper cites Challenges in evaluating AI systems, 2023.

How Benchmark Prediction from Fewer Data Misses the Mark Challenges in evaluating AI systems, 2023

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.747484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fef75381-5e12-4c9b-8e76-4763ed271a4e · outbound

This paper cites The third PASCAL recognizing textual entailment challenge.

How Benchmark Prediction from Fewer Data Misses the Mark The third PASCAL recognizing textual entailment challenge

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.737314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e92bd0f6-4ca0-4717-9731-1ee70e48c1e3 · outbound

This paper cites An introduction to the augmented inverse propensity weighted estimator.

How Benchmark Prediction from Fewer Data Misses the Mark An introduction to the augmented inverse propensity weighted estimator

Reference 19

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Source-reported events for the cited work

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Observation f75e2726-15af-4092-bb23-e4c5e4ba78b6 · outbound

This paper cites Great Models Think Alike and this Undermines AI Oversight.

How Benchmark Prediction from Fewer Data Misses the Mark Great Models Think Alike and this Undermines AI Oversight

Reference 20

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Source-reported events for the cited work

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Observation 011eccdc-8773-4472-a298-376579d64cd4 · outbound

This paper cites A Survey on LLM-as-a-Judge.

How Benchmark Prediction from Fewer Data Misses the Mark A Survey on LLM-as-a-Judge

Reference 21

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Observation 9a4172e1-1add-4cf7-9d9a-ff19aaaa9c42 · outbound

This paper cites Ho, Christopher Ré, Adam Chilton, Aditya Narayana, Alex Chohlas-Wood, Austin Peters, Brandon Waldon, Daniel N.

How Benchmark Prediction from Fewer Data Misses the Mark Ho, Christopher Ré, Adam Chilton, Aditya Narayana, Alex Chohlas-Wood, Austin Peters, Brandon Waldon, Daniel N

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.719690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e236eb94-6ade-4c5c-8d7c-372701a630ef · outbound

This paper cites Is GPT-4 a reliable rater? Evaluating Consistency in GPT-4 Text Ratings.

How Benchmark Prediction from Fewer Data Misses the Mark Is GPT-4 a reliable rater? Evaluating Consistency in GPT-4 Text Ratings

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:34:27.482516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9d271dc8-53d5-4f79-a88a-b7ed61a6cf7f · outbound

This paper cites Test-Time Training on Nearest Neighbors for Large Language Models.

How Benchmark Prediction from Fewer Data Misses the Mark Test-Time Training on Nearest Neighbors for Large Language Models

Reference 24

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Source-reported events for the cited work

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Observation 28058209-5f05-40f2-aee6-3bd86c0af9a6 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

How Benchmark Prediction from Fewer Data Misses the Mark Measuring Massive Multitask Language Understanding

Reference 25

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Source-reported events for the cited work

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Observation e3adc9fc-5581-4631-8821-dd64ac57f168 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

How Benchmark Prediction from Fewer Data Misses the Mark Measuring Mathematical Problem Solving With the MATH Dataset

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T05:34:26.894434Z

Source-reported events for the cited work

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Observation f0802203-fea5-4bf8-8d0a-3f8e4ffbd548 · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

How Benchmark Prediction from Fewer Data Misses the Mark Categorical Reparameterization with Gumbel-Softmax

Reference 27

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Observation 87a8b5ae-eb96-4c08-9469-491f0d59427c · outbound

This paper cites What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams.

How Benchmark Prediction from Fewer Data Misses the Mark What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams

Reference 28

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Observation 2f0551a2-b906-4595-bfc7-fbf5e87d54f4 · outbound

This paper cites Scaling Laws for Neural Language Models.

How Benchmark Prediction from Fewer Data Misses the Mark Scaling Laws for Neural Language Models

Reference 29

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Observation 31b45bc4-f92a-41a9-91b7-4d3ab770d763 · outbound

This paper cites Active testing: Sample- efficient model evaluation.

How Benchmark Prediction from Fewer Data Misses the Mark Active testing: Sample- efficient model evaluation

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0337a2e4-f01d-4d11-bdf5-60f2c848bf25 · outbound

This paper cites Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation.

How Benchmark Prediction from Fewer Data Misses the Mark Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7fc985a9-dc69-49a8-8d29-7754f35819ff · outbound

This paper cites Retrieval- augmented generation for knowledge-intensive nlp tasks.

How Benchmark Prediction from Fewer Data Misses the Mark Retrieval- augmented generation for knowledge-intensive nlp tasks

Reference 32

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Observation 75fea7a5-cf22-4c32-a251-182db853b17e · outbound

This paper cites Active Evaluation Acquisition for Efficient LLM Benchmarking.

How Benchmark Prediction from Fewer Data Misses the Mark Active Evaluation Acquisition for Efficient LLM Benchmarking

Reference 33

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Observation 423e05f4-9dde-4337-9584-8a968046cef3 · outbound

This paper cites Manning, Christopher R’e, Diana Acosta-Navas, Drew A.

How Benchmark Prediction from Fewer Data Misses the Mark Manning, Christopher R’e, Diana Acosta-Navas, Drew A

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.692361Z

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Observation 5705a10f-9f73-4de9-ac11-01fc409cea29 · outbound

This paper cites Quantifying Variance in Evaluation Benchmarks.

How Benchmark Prediction from Fewer Data Misses the Mark Quantifying Variance in Evaluation Benchmarks

Reference 35

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Observation c16dfc68-05f7-407b-8512-b586c565db80 · outbound

This paper cites Model Similarity Mitigates Test Set Overuse.

How Benchmark Prediction from Fewer Data Misses the Mark Model Similarity Mitigates Test Set Overuse

Reference 36

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Observation ef43aa4b-2f20-4461-a61c-8771e5acea8b · outbound

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

How Benchmark Prediction from Fewer Data Misses the Mark Can a suit of armor conduct electricity? a new dataset for open book question answering

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.682228Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 080a3031-0e61-44d9-a8f4-1d99a863ec85 · outbound

This paper cites How predictable is language model benchmark performance?.

How Benchmark Prediction from Fewer Data Misses the Mark How predictable is language model benchmark performance?

Reference 38

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Observation bf06c144-35d8-4081-ae7f-9f0e12da39ea · outbound

This paper cites PredictaBoard: Benchmarking LLM Score Predictability.

How Benchmark Prediction from Fewer Data Misses the Mark PredictaBoard: Benchmarking LLM Score Predictability

Reference 39

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Observation 30ef8940-83f3-4a49-8f21-17c2e82ebbe2 · outbound

This paper cites LLM Evaluators Recognize and Favor Their Own Generations.

How Benchmark Prediction from Fewer Data Misses the Mark LLM Evaluators Recognize and Favor Their Own Generations

Reference 40

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Observation 7d31cfa5-4838-48d6-b3ba-5e84408948d3 · outbound

This paper cites tinyBenchmarks: evaluating LLMs with fewer examples.

How Benchmark Prediction from Fewer Data Misses the Mark tinyBenchmarks: evaluating LLMs with fewer examples

Reference 41

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Observation 98b3f156-3235-4eb2-82b4-51d6389c886e · outbound

This paper cites Efficient multi-prompt evaluation of LLMs.

How Benchmark Prediction from Fewer Data Misses the Mark Efficient multi-prompt evaluation of LLMs

Reference 42

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Observation c2570af8-3b21-4c0a-af04-5be2ba7ee595 · outbound

This paper cites SQuAD: 100,000+ questions for machine comprehension of text.

How Benchmark Prediction from Fewer Data Misses the Mark SQuAD: 100,000+ questions for machine comprehension of text

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.671738Z

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Observation ed27291c-1724-485f-8517-1abc5e842edb · outbound

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

How Benchmark Prediction from Fewer Data Misses the Mark GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 44

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Observation 2f7c9bc9-ee91-4177-a4b8-dd0edfd42832 · outbound

This paper cites Semiparametric efficiency in multivariate regression models with missing data.

How Benchmark Prediction from Fewer Data Misses the Mark Semiparametric efficiency in multivariate regression models with missing data

Reference 45

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Observation 2738fefe-3532-4e29-96a8-666d66c99118 · outbound

This paper cites Lalor, Robin Jia, and Jordan L.

How Benchmark Prediction from Fewer Data Misses the Mark Lalor, Robin Jia, and Jordan L

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.655964Z

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Observation b515bf99-dcf7-4650-b11c-3290558b5a87 · outbound

This paper cites Observational Scaling Laws and the Predictability of Language Model Performance.

How Benchmark Prediction from Fewer Data Misses the Mark Observational Scaling Laws and the Predictability of Language Model Performance

Reference 47

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Observation b60afbc5-1bd7-4340-ad53-90e5ba059210 · outbound

This paper cites Bernstein, Alexander C.

How Benchmark Prediction from Fewer Data Misses the Mark Bernstein, Alexander C

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.646469Z

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Observation 6da416b0-5c7d-4d36-ae7e-bb0990390be9 · outbound

This paper cites Data distillation: A survey.

How Benchmark Prediction from Fewer Data Misses the Mark Data distillation: A survey

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.636832Z

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Observation 1e72658b-55a9-4ae9-a9f7-4698ca13d93c · outbound

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

How Benchmark Prediction from Fewer Data Misses the Mark Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 50

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Observation 97e17da9-1936-4277-9f66-41108ef856ea · outbound

This paper cites Recursive deep models for semantic compositionality over a sentiment treebank.

How Benchmark Prediction from Fewer Data Misses the Mark Recursive deep models for semantic compositionality over a sentiment treebank

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.627625Z

Source-reported events for the cited work

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Observation 0ffbcf20-b91c-480a-9863-65da0b63a71c · outbound

This paper cites MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning.

How Benchmark Prediction from Fewer Data Misses the Mark MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning

Reference 52

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Source-reported events for the cited work

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Observation fad17933-bd87-4d4a-910f-86803c968dd7 · outbound

This paper cites Test-time training with self-supervision for generalization under distribution shifts.

How Benchmark Prediction from Fewer Data Misses the Mark Test-time training with self-supervision for generalization under distribution shifts

Reference 53

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Observation a1446e7b-b1a7-4633-a1a4-c2523c4af7a0 · outbound

This paper cites Le, Ed H.

How Benchmark Prediction from Fewer Data Misses the Mark Le, Ed H

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.610789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6a3b525b-d889-45a6-8b7c-05356aa14d5e · outbound

This paper cites Four lectures on probabilistic methods for data science.

How Benchmark Prediction from Fewer Data Misses the Mark Four lectures on probabilistic methods for data science

Reference 55

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This paper cites Anchor Points: Benchmarking Models with Much Fewer Examples.

How Benchmark Prediction from Fewer Data Misses the Mark Anchor Points: Benchmarking Models with Much Fewer Examples

Reference 56

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How Benchmark Prediction from Fewer Data Misses the Mark Unresolved cited work

Reference 57

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

How Benchmark Prediction from Fewer Data Misses the Mark MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 58

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This paper cites Self-Preference Bias in LLM-as-a-Judge.

How Benchmark Prediction from Fewer Data Misses the Mark Self-Preference Bias in LLM-as-a-Judge

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T05:34:26.999567Z

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How Benchmark Prediction from Fewer Data Misses the Mark Unresolved cited work

Reference 60

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This paper cites Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models.

How Benchmark Prediction from Fewer Data Misses the Mark Skill-Mix: a Flexible and Expandable Family of Evaluations for AI models

Reference 61

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This paper cites Automatic evaluation of attribution by large language models.

How Benchmark Prediction from Fewer Data Misses the Mark Automatic evaluation of attribution by large language models

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:34:27.582423Z

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Observation 9cbd5450-d806-4bfd-8b28-5ab478823d84 · outbound

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

How Benchmark Prediction from Fewer Data Misses the Mark Instruction-Following Evaluation for Large Language Models

Reference 63

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Observation e8a454e4-155c-44a6-93ef-63ffc29a4e1c · outbound

This paper cites On Speeding Up Language Model Evaluation.

How Benchmark Prediction from Fewer Data Misses the Mark On Speeding Up Language Model Evaluation

Reference 64

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This paper cites Probabilistic Bilevel Coreset Selection.

How Benchmark Prediction from Fewer Data Misses the Mark Probabilistic Bilevel Coreset Selection

Reference 65

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:34:27.059462Z

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This paper cites How to select datapoints for efficient human evaluation of nlg models?, 2025.

How Benchmark Prediction from Fewer Data Misses the Mark How to select datapoints for efficient human evaluation of nlg models?, 2025

Reference 66

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Pith citing papers

Observation b27a9196-c3e0-4eb4-9adc-6acd9af2d40c · inbound

Efficient Evaluation of LLM Performance with Statistical Guarantees cites this paper.

Efficient Evaluation of LLM Performance with Statistical Guarantees How Benchmark Prediction from Fewer Data Misses the Mark

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-22T01:23:30.685296Z

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Observation 6992b4b1-fe2e-435b-85da-cacb5393970f · inbound

Towards Reliable LLM Evaluation: Correcting the Winner's Curse in Adaptive Benchmarking cites this paper.

Towards Reliable LLM Evaluation: Correcting the Winner's Curse in Adaptive Benchmarking How Benchmark Prediction from Fewer Data Misses the Mark

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-07-22T01:23:30.685296Z

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FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences cites this paper.

FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences How Benchmark Prediction from Fewer Data Misses the Mark

Reference 60

Resolution: metadata mismatch; arxiv_id, observed 2026-07-22T01:23:30.685296Z

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Validity Threats for Foundation Model Research cites this paper.

Validity Threats for Foundation Model Research How Benchmark Prediction from Fewer Data Misses the Mark

Reference 110

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Observation cf978fc5-69ca-48b4-a646-c55b8a9574af · inbound

You Don't Need to Run Every Eval cites this paper.

You Don't Need to Run Every Eval How Benchmark Prediction from Fewer Data Misses the Mark

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-07-22T01:23:30.685296Z

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Observation 497f56ee-452b-4e85-b947-3c7cecea0230 · inbound

Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification cites this paper.

Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification How Benchmark Prediction from Fewer Data Misses the Mark

Reference 55

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