Paper Citation Record · LEDGER

AI-Assisted Generation of Difficult Math Questions

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2407.21009.

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

pith.paper-citation-record.v1
2407.21009 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:26:07.650297Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:40:08.252506Z

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 38a6366a-6e42-4c3d-98d4-564a258771ae · inbound

MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations cites this paper.

MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations AI-Assisted Generation of Difficult Math Questions

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-08T15:26:07.650297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f40ecc08-d9b2-4de1-9432-aea3ac958b87 · inbound

Automated Capability Discovery via Foundation Model Self-Exploration cites this paper.

Automated Capability Discovery via Foundation Model Self-Exploration AI-Assisted Generation of Difficult Math Questions

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-08T12:17:55.334815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a360c43b-a2f8-4f0a-a27a-f9c218938788 · inbound

Temporalizing Confidence: Evaluation of Chain-of-Thought Reasoning with Signal Temporal Logic cites this paper.

Temporalizing Confidence: Evaluation of Chain-of-Thought Reasoning with Signal Temporal Logic AI-Assisted Generation of Difficult Math Questions

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T05:19:03.281512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c68717eb-3c15-4e57-a78b-6f5bfcb3fafb · inbound

Automatically Generating Hard Math Problems from Hypothesis-Driven Error Analysis cites this paper.

Automatically Generating Hard Math Problems from Hypothesis-Driven Error Analysis AI-Assisted Generation of Difficult Math Questions

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:40:49.945602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cc984434-c759-4496-bf35-b7f631a54615 · inbound

Human vs Machine Mathematical Difficulty on Project Euler: An Experimental Analysis cites this paper.

Human vs Machine Mathematical Difficulty on Project Euler: An Experimental Analysis AI-Assisted Generation of Difficult Math Questions

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:19:44.142546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ce52f3fc-7f6c-441b-ba7d-2c718e06c498 · inbound

Autodata: An agentic data scientist to create high quality synthetic data cites this paper.

Autodata: An agentic data scientist to create high quality synthetic data AI-Assisted Generation of Difficult Math Questions

Reference 109

Resolution: verified exact; arxiv_id, observed 2026-07-04T20:40:08.254047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c42a05ce-2a12-4088-ae2f-eecf831c8c31 · inbound

Autodata: An agentic data scientist to create high quality synthetic data cites this paper.

Autodata: An agentic data scientist to create high quality synthetic data AI-Assisted Generation of Difficult Math Questions

Reference 109

Resolution: verified exact; arxiv_id, observed 2026-07-04T13:19:51.242672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 40e65629-249f-4b0f-b6f3-1ab1aaa7a951 · inbound

Autodata: An agentic data scientist to create high quality synthetic data cites this paper.

Autodata: An agentic data scientist to create high quality synthetic data AI-Assisted Generation of Difficult Math Questions

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-12T12:08:06.206832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3769a8a2-921a-42ea-a2ef-7aab904d3024 · inbound

Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning cites this paper.

Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning AI-Assisted Generation of Difficult Math Questions

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T04:30:43.301574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.