Paper Citation Record · LEDGER

Quantifying Variance in Evaluation Benchmarks

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 28 inbound Pith citation observations for arXiv:2406.10229.

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

pith.paper-citation-record.v1
2406.10229 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 28 of 28 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:07:23.253277Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
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External citation measurements

1
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8d45ccfe-0234-4aa6-855a-01ac7411d9b5 · inbound

Loss-to-Loss Prediction: Scaling Laws for All Datasets cites this paper.

Loss-to-Loss Prediction: Scaling Laws for All Datasets Quantifying Variance in Evaluation Benchmarks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T17:07:23.253277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbf2f91f-8564-41de-9c90-e3d558a412da · inbound

Lost in Inference: Rediscovering the Role of Natural Language Inference for Large Language Models cites this paper.

Lost in Inference: Rediscovering the Role of Natural Language Inference for Large Language Models Quantifying Variance in Evaluation Benchmarks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T15:35:58.924818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5488d2ae-4920-460d-ac76-f9f5a45a5486 · inbound

Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic cites this paper.

Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic Quantifying Variance in Evaluation Benchmarks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T21:37:27.003075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cb9b301-1ad3-4483-95a2-a675fc773bc9 · inbound

HARP: A challenging human-annotated math reasoning benchmark cites this paper.

HARP: A challenging human-annotated math reasoning benchmark Quantifying Variance in Evaluation Benchmarks

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T17:34:19.733078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b1e145f-0b0c-4cbf-938e-5aa28133c018 · inbound

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws cites this paper.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Quantifying Variance in Evaluation Benchmarks

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.051526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b0ea9157-82e9-4f2d-b003-8f3f5de0b70d · inbound

A Study of LLMs' Preferences for Libraries and Programming Languages cites this paper.

A Study of LLMs' Preferences for Libraries and Programming Languages Quantifying Variance in Evaluation Benchmarks

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-22T22:55:12.391707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bc38713e-f5ab-4a85-86d9-645a25d093ce · inbound

MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks cites this paper.

MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks Quantifying Variance in Evaluation Benchmarks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T13:55:56.617692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bef963a3-d7a2-4043-aa23-ec9d4c5e89aa · inbound

Structure-Aware Fill-in-the-Middle Pretraining for Code cites this paper.

Structure-Aware Fill-in-the-Middle Pretraining for Code Quantifying Variance in Evaluation Benchmarks

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T12:14:23.000112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5238fcf6-3d49-42d7-95d7-957763d3bf49 · inbound

Beyond Text Compression: Evaluating Tokenizers Across Scales cites this paper.

Beyond Text Compression: Evaluating Tokenizers Across Scales Quantifying Variance in Evaluation Benchmarks

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T11:18:10.760594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

How Benchmark Prediction from Fewer Data Misses the Mark cites this paper.

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

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T05:34:26.922228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffa8195f-6c42-430e-a18d-f5c38ae41b92 · inbound

FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language cites this paper.

FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language Quantifying Variance in Evaluation Benchmarks

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T22:46:40.311443Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2e17ed5-befc-4d79-9cb8-7275906204c8 · inbound

Fluid Language Model Benchmarking cites this paper.

Fluid Language Model Benchmarking Quantifying Variance in Evaluation Benchmarks

Reference 1983

Resolution: unresolved; no resolver link, observed 2026-08-04T17:10:43.439511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c0953d6-f1b9-4059-9f27-cdadff721879 · inbound

Correcting Prompt Dependence in LLM Benchmarks: A Bayesian Hierarchical Model with Embedding-Space Clustering cites this paper.

Correcting Prompt Dependence in LLM Benchmarks: A Bayesian Hierarchical Model with Embedding-Space Clustering Quantifying Variance in Evaluation Benchmarks

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T11:19:47.532043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38ae454e-4f7b-403d-a66c-cc2cee262063 · inbound

The Art of Scaling Reinforcement Learning Compute for LLMs cites this paper.

The Art of Scaling Reinforcement Learning Compute for LLMs Quantifying Variance in Evaluation Benchmarks

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.003804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 976900a8-7ad3-4a51-b8f9-ca3797e671d2 · inbound

ReasonBENCH: Benchmarking the (In)Stability of LLM Reasoning cites this paper.

ReasonBENCH: Benchmarking the (In)Stability of LLM Reasoning Quantifying Variance in Evaluation Benchmarks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T17:53:55.247281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f3ce2f6-22e1-4faa-a8a2-1ac5f18decc0 · inbound

A Tale of Two Variances: When Single-Seed Benchmarks Fail in Bayesian Deep Learning cites this paper.

A Tale of Two Variances: When Single-Seed Benchmarks Fail in Bayesian Deep Learning Quantifying Variance in Evaluation Benchmarks

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T20:36:11.793707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 507f4142-380b-435a-aab5-d0e2331614df · inbound

Beyond Fixed Benchmarks and Worst-Case Attacks: Dynamic Boundary Evaluation for Language Models cites this paper.

Beyond Fixed Benchmarks and Worst-Case Attacks: Dynamic Boundary Evaluation for Language Models Quantifying Variance in Evaluation Benchmarks

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:06:13.592425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8bbafc1c-e21b-4b90-93a7-af375f2bddfd · inbound

The Harder Text Embedding Benchmark (HTEB): Beyond One-dimensional Static Robustness cites this paper.

The Harder Text Embedding Benchmark (HTEB): Beyond One-dimensional Static Robustness Quantifying Variance in Evaluation Benchmarks

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-06-29T12:43:25.039511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 94b16516-8599-4888-a6ea-8900097459f0 · inbound

Resolution Diagnostics for Paired LLM Evaluation cites this paper.

Resolution Diagnostics for Paired LLM Evaluation Quantifying Variance in Evaluation Benchmarks

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-06-29T07:23:13.254548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 30606fe5-3738-4848-a408-cc5b42f6d912 · inbound

Validity Threats for Foundation Model Research cites this paper.

Validity Threats for Foundation Model Research Quantifying Variance in Evaluation Benchmarks

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:36:44.879438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 93cf316e-d257-42ee-b052-52a7e1bda135 · inbound

Bounded Difference Concentration for Infinitely Exchangeable Sequences with Applications to AI Benchmark Uncertainty cites this paper.

Bounded Difference Concentration for Infinitely Exchangeable Sequences with Applications to AI Benchmark Uncertainty Quantifying Variance in Evaluation Benchmarks

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T23:09:00.963089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6d6830a2-a2fa-452e-af6b-407fe573777f · inbound

DataComp-VLM: Improved Open Datasets for Vision-Language Models cites this paper.

DataComp-VLM: Improved Open Datasets for Vision-Language Models Quantifying Variance in Evaluation Benchmarks

Reference 197

Resolution: verified exact; arxiv_id, observed 2026-07-01T15:45:47.638431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cffd2844-7096-4c59-a7f3-1a2b2b322c4f · inbound

DataComp-VLM: Improved Open Datasets for Vision-Language Models cites this paper.

DataComp-VLM: Improved Open Datasets for Vision-Language Models Quantifying Variance in Evaluation Benchmarks

Reference 197

Resolution: verified exact; arxiv_id, observed 2026-07-02T21:17:23.993727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7c690aa6-92c3-42d1-886a-5b6d566a38d6 · inbound

How Much of the Routing Gap Is Real? Decomposing the Router-to-Oracle Gap into Reproducible Specialist Advantage and Single-Draw Label Noise cites this paper.

How Much of the Routing Gap Is Real? Decomposing the Router-to-Oracle Gap into Reproducible Specialist Advantage and Single-Draw Label Noise Quantifying Variance in Evaluation Benchmarks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-12T02:29:58.886380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96468dd4-d445-4fba-b111-cd7f773baf37 · inbound

DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks cites this paper.

DeepSWE: Measuring Frontier Coding Agents on Original, Long-Horizon Engineering Tasks Quantifying Variance in Evaluation Benchmarks

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-07-10T15:07:19.640776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation daf1a889-df9e-4597-bf7c-34b3b488455c · inbound

AdaRoPE: Not All Attention Heads Should Rotate and Scale Equally cites this paper.

AdaRoPE: Not All Attention Heads Should Rotate and Scale Equally Quantifying Variance in Evaluation Benchmarks

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-02T12:17:16.303388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c876188-bedd-4059-9116-3271153f8224 · inbound

Laplace-PSN-IRT: Uncertainty Quantification for Neural Item Response Theory Models of LLM Benchmarks cites this paper.

Laplace-PSN-IRT: Uncertainty Quantification for Neural Item Response Theory Models of LLM Benchmarks Quantifying Variance in Evaluation Benchmarks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T03:00:48.305722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c82b8d54-7f25-4e7d-a7c0-596b29880703 · inbound

Messier: A High-Resolution Corpus for Cross-Benchmark Agent Evaluation cites this paper.

Messier: A High-Resolution Corpus for Cross-Benchmark Agent Evaluation Quantifying Variance in Evaluation Benchmarks

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-01T01:16:44.479430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.