Paper Citation Record · LEDGER

Position: AI Evaluation Should Learn from How We Test Humans

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2306.10512.

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

pith.paper-citation-record.v1
2306.10512 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:57:02.199615Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T22:00:41.508241Z

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 8707592e-c62f-4339-83ba-6007f190d26f · inbound

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models cites this paper.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Position: AI Evaluation Should Learn from How We Test Humans

Reference 250

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:02.199615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e07450a-5866-43fd-8161-ac1d8c509204 · inbound

The potential -- and the pitfalls -- of using pre-trained language models as cognitive science theories cites this paper.

The potential -- and the pitfalls -- of using pre-trained language models as cognitive science theories Position: AI Evaluation Should Learn from How We Test Humans

Reference 112

Resolution: unresolved; no resolver link, observed 2026-08-10T17:01:28.302975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 912f4b53-cf7d-48e8-92eb-8b8aad5c6f93 · inbound

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes cites this paper.

Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypes Position: AI Evaluation Should Learn from How We Test Humans

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:19.226127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68778ea7-cdff-46c2-b429-b893d4500153 · inbound

Psychometric-Based Evaluation for Theorem Proving with Large Language Models cites this paper.

Psychometric-Based Evaluation for Theorem Proving with Large Language Models Position: AI Evaluation Should Learn from How We Test Humans

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T17:36:09.647330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05ed1da9-f499-4840-9920-0ff28eff1f3c · inbound

AutoJudger: An Agent-Driven Framework for Efficient Benchmarking of MLLMs cites this paper.

AutoJudger: An Agent-Driven Framework for Efficient Benchmarking of MLLMs Position: AI Evaluation Should Learn from How We Test Humans

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-07T13:40:25.866299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fc827b1-b721-4472-8bcc-617ffe7d9033 · inbound

Fluid Language Model Benchmarking cites this paper.

Fluid Language Model Benchmarking Position: AI Evaluation Should Learn from How We Test Humans

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-04T17:10:43.454204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ce79528-af9e-4bc9-a45c-9b7b9f3488d5 · inbound

Position: AI Evaluations Should be Grounded on a Theory of Capability cites this paper.

Position: AI Evaluations Should be Grounded on a Theory of Capability Position: AI Evaluation Should Learn from How We Test Humans

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-21T22:00:41.510890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3cbdb515-ecc6-42aa-84ee-abe526a966b9 · inbound

Adaptive Testing for LLM Evaluation: A Psychometric Alternative to Static Benchmarks cites this paper.

Adaptive Testing for LLM Evaluation: A Psychometric Alternative to Static Benchmarks Position: AI Evaluation Should Learn from How We Test Humans

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-04T08:10:10.570262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b116e399-813e-4881-a96a-e464b8e0b6a1 · inbound

FairTree: Subgroup Fairness Auditing of Machine Learning Models with Bias-Variance Decomposition cites this paper.

FairTree: Subgroup Fairness Auditing of Machine Learning Models with Bias-Variance Decomposition Position: AI Evaluation Should Learn from How We Test Humans

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:31:03.899427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5504ef60-7b19-44d4-9713-039aaca1b0b4 · inbound

Beyond the Mean: Within-Model Reliable Change Detection for LLM Evaluation cites this paper.

Beyond the Mean: Within-Model Reliable Change Detection for LLM Evaluation Position: AI Evaluation Should Learn from How We Test Humans

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:06:27.320015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.