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Paper Citation Record · LEDGER

Can Large Language Models Always Solve Easy Problems if They Can Solve Harder Ones?

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.12809.

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

pith.paper-citation-record.v1
2406.12809 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:58:59.131998Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T09:09:15.069829Z

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 f8baea53-1534-4f46-8b4d-0b2b7d06328e · inbound

Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models cites this paper.

Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models Can Large Language Models Always Solve Easy Problems if They Can Solve Harder Ones?

Reference 75

Resolution
verified exact
arxiv_id, observed 2026-05-15T09:09:15.071639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-05-15T09:09:14.884516Z digest=sha256:9567215fab2e555e83db7f0bebf4a3adb7a8a992a51beb8fbd7f9db2595d2b99

Observation 5a4d94a7-1ef6-48ea-abdf-01f596c561c7 · inbound

Learning Model Successors cites this paper.

Learning Model Successors Can Large Language Models Always Solve Easy Problems if They Can Solve Harder Ones?

Reference 186

Resolution
unresolved
no resolver link, observed 2026-08-09T19:58:59.131998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T19:58:59.131998Z digest=sha256:789a45d0a1ff70f57dbdb3ce7d52bd37134e9434d165dbdee421a4aa8f6ce4c5