Paper Citation Record · LEDGER

Easy Problems That LLMs Get Wrong

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2405.19616.

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

pith.paper-citation-record.v1
2405.19616 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:51:53.430420Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T21:28:34.144017Z

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 f8caf0c7-47b3-4747-8c2e-6e1c9740ad3a · inbound

LLM-Based Config Synthesis requires Disambiguation cites this paper.

LLM-Based Config Synthesis requires Disambiguation Easy Problems That LLMs Get Wrong

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T16:51:53.430420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d55956a8-98ff-469e-b2e9-6fa74ff5606f · inbound

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution cites this paper.

Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution Easy Problems That LLMs Get Wrong

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-05T22:21:05.930325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2b77017-a89f-4fd5-9fd0-a40d8177ee2a · inbound

Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning cites this paper.

Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning Easy Problems That LLMs Get Wrong

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-05T21:10:36.705823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6270435-135b-46d6-a83e-1bf3a0c09e96 · inbound

Evaluating Code Reasoning Abilities of Large Language Models Under Real-World Settings cites this paper.

Evaluating Code Reasoning Abilities of Large Language Models Under Real-World Settings Easy Problems That LLMs Get Wrong

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-05-16T21:28:34.145638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.