FrontierMath: Erdős (Lean 4)

What it measures

68 unsolved Erdős problems where AI systems must produce formally verified solutions in Lean 4.

Reading the measurement

A correct final answer, a complete proof and useful research progress are different achievements. Choose the one the task actually grades. Inspect the permitted computation, attempt budget and handling of invalid arguments before comparing models.

Evidence status

Catalogued from the supplied benchmark research notes. Publisher directories, where linked, are discovery references. Numerical model results are shown only when a source dataset has been imported; a catalog entry alone does not establish an independently reviewed evaluation.

Epoch data and evaluation setting

Epoch AI publishes this benchmark in its capability collection. Imported rows retain the published score column and model configuration. Original authors and evaluation providers remain credited in the result references.

Epoch attribution and score coverage

Data is attributed to Epoch AI under CC BY 4.0, with original evaluation providers credited in the linked source snapshots. Scores are imported only where a published mapping identifies the field and its scale; missing or incompatible records are omitted rather than assigned zero.

Where this fits in an evaluation suite

Final-answer accuracy, proof credit, and expert-reviewed research progress are distinct signals. A proof-aware evaluation should retain partial work and audit strong claims.

Start with a capability hypothesis, then choose a second test with a different failure mode. Keep the checkpoint, reasoning effort, agent tools, sampling policy, and scoring procedure fixed when measuring a training intervention. Record failures and missing outputs in the denominator.

A reproducible comparison

Record the source publication, benchmark version, scoring units, denominator, tool access and attempt budget. Preserve uncertainty and distinguish developer-reported results from independent evaluations.

Publish the exact run configuration, item coverage, score units, and available uncertainty. If a source supplies only an aggregate or a single run, preserve that limit. A new model name or a rounded score alone does not establish a capability improvement.