Paper Citation Record · LEDGER

When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2402.01781.

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

pith.paper-citation-record.v1
2402.01781 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 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 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:33:23.929114Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T10:47:31.141279Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation fba070fe-dd71-477f-9785-f490b8588e91 · inbound

RouterBench: A Benchmark for Multi-LLM Routing System cites this paper.

RouterBench: A Benchmark for Multi-LLM Routing System When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-05-16T10:47:31.143539Z

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 a93ec630-105a-4a4c-a428-285fb95e7f65 · inbound

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

MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:51:06.285386Z

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 9e97d5ea-bf91-479e-a756-e624b0e3e64d · inbound

BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices cites this paper.

BetterBench: Assessing AI Benchmarks, Uncovering Issues, and Establishing Best Practices When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T17:01:31.249702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec67dca3-9d33-4a33-bbc2-7c9dbcbce281 · 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 When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T21:37:26.884869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fd1be3c-d545-4ff7-98b1-2696f4ee6316 · inbound

Too Big to Fool: Resisting Deception in Language Models cites this paper.

Too Big to Fool: Resisting Deception in Language Models When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T15:55:23.051934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc6b0e4d-8ae7-4e3b-b0c6-2ed46a0dbf33 · inbound

Evaluating LLM Reasoning in the Operations Research Domain with ORQA cites this paper.

Evaluating LLM Reasoning in the Operations Research Domain with ORQA When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T06:01:13.224508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc8883a8-ff25-4446-a322-8650f92e5d4f · inbound

A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks cites this paper.

A Survey of Theory of Mind in Large Language Models: Evaluations, Representations, and Safety Risks When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T15:22:44.788604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c1b2e4c-07fe-4278-963b-01609ebcdb51 · inbound

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

Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T15:06:54.722646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6aabf32b-0f38-4f7f-9150-377d5b946014 · inbound

Automatic Evaluation of Healthcare LLMs Beyond Question-Answering cites this paper.

Automatic Evaluation of Healthcare LLMs Beyond Question-Answering When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T14:46:29.541762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8075e7fb-d2d2-4739-9b6d-eb560a6331db · inbound

RoToR: Towards More Reliable Responses for Order-Invariant Inputs cites this paper.

RoToR: Towards More Reliable Responses for Order-Invariant Inputs When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-08T16:11:43.090058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d59d5cb2-0912-422a-a0cf-c2dece4483e3 · inbound

ZeroSumEval: Scaling LLM Evaluation with Inter-Model Competition cites this paper.

ZeroSumEval: Scaling LLM Evaluation with Inter-Model Competition When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T12:33:23.929114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d2fff4b-062f-4cdd-a428-2e2513130e46 · inbound

Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism cites this paper.

Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:29.300022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d6a5638-e8f3-4e0e-9d76-0c8b5cbfff94 · inbound

Quantifying Memory Utilization with Effective State-Size cites this paper.

Quantifying Memory Utilization with Effective State-Size When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T05:58:22.018300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67b6341e-63f4-46a0-8f7d-e48e562108f8 · inbound

Instrument Effects in Language-Model Honesty Evaluation: An Auditable Single-System Demonstration cites this paper.

Instrument Effects in Language-Model Honesty Evaluation: An Auditable Single-System Demonstration When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T02:14:08.103425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.