Pundit ClaimsAI claims by Gary Marcus and Ed Zitron, with the evidence

“The results were dismal: None of the AIs scored higher than 5% overall.”

Reports of LLMs mastering math have been greatly exaggerated, paragraph 1. The full statement is at the source.

Our reading, the sentence that was checked:

On the 2025 USAMO, none of the leading reasoning LLMs tested scored higher than 5% overall.

Assessment: Supported Evidence as of

Where and when Gary Marcus wrote it

Marcus on AI, Reports of LLMs mastering math have been greatly exaggerated, paragraph 1.

Said
Source observed
live page, full text, public
Evidence as of
the cutoff for this assessment
Assessment recorded
assessment marcus-reports-of-llms-mastering-math-have-552698-a1

We recorded 11 claims from this post. See every claim from this post.

The wording above is the version this site observed on 2026-10-28. The source may have been edited after 2025-04-05. This page does not claim the wording was captured then.

What the claim commits to

Kind
Observation
Deadline, as the speaker wrote it
None stated. This site does not add one.
Deadline state when assessed
No horizon
Deadline state on 2026-10-29
No horizon (the release’s reference date)

Conditions

  • USAMO 2025 problems (qualifier, stated by the speaker)
  • proofs evaluated by experts (qualifier, stated by the speaker)

From the speaker

  • None of the AIs
  • higher than 5% overall
  • USAMO
  • proofs output by all these models were evaluated by experts

Our interpretation

  • The claim concerns the six named models in the original March 2025 evaluation.
  • Overall means the aggregate expert-assigned points, averaged across evaluation runs and divided by the maximum possible points. It does not mean the percentage of problems completely solved.
  • Later models and later evaluations are outside this claim.

Counts as supported if: Every named model received an aggregate average score no greater than 5% of the available points in the original evaluation.

Counts as contradicted if: At least one named model received an aggregate average score greater than 5% in that evaluation.

Retrospective: these criteria were written after the outcome was public.

Current assessment

Was it true for the period described?

Outcome: Supported

The dated evidence matches what the claim committed to, under the criteria frozen before grading.

Scored on a plain reading. The speaker named no measure for this claim, so it was judged on the reading an informed reader would take, stated below under our interpretation. Both reviews had to find that reading fair.

Evidence as of
Deadline state when assessed
No horizon
Assessment recorded
Policy version
2026-10-28.7

Rationale

The original evaluation's highest average total was 2.0 points out of 42, approximately 4.76%. All six models Marcus named were therefore below his 5% threshold.

Limitations

  • This describes the original evaluation with its particular prompts, sampling procedure and expert grading.
  • It does not establish a ceiling on these models under other methods or on subsequently released models.

Evidence (1 item)

  • Supports the claim: Petrov et al., ETH Zurich and INSAIT; arXiv,

    four runs

    Why it matters here: Table 1 reports average totals of 2.0, 1.8, 1.5, 1.2, 1.2 and 0.9 points for the six named models, against a maximum of 42. The highest reported total therefore corresponds to approximately 4.76%, below 5%.

    Publisher
    Petrov et al., ETH Zurich and INSAIT; arXiv
    Kind of source
    Preprint, not peer reviewed
    Arithmetic
    100 × max(2.0, 1.8, 1.5, 1.2, 1.2, 0.9) / 42 (inputs: Table 1 displayed totals: R1 2.0; Flash-Thinking 1.8; Claude 3.7 1.5; QwQ 1.2; o1-pro 1.2; o3-mini 0.9; Maximum total: 42 points; Approximately 4.76%, calculated from the displayed totals. Their rounding does not put the maximum above 5%.)
    Describes events of
    Public from

Where it was said (1)

Said once in the sources this register covers.

  • : Marcus on AI, Reports of LLMs mastering math have been greatly exaggerated

    The results were dismal: None of the AIs scored higher than 5% overall.

    Published
    2025-04-05
    Source observed
    , live page
    Access
    full text, public

History

Assessment versions

Recorded events

  1. Assessment publishedBy this site

    First assessment published after both reviews and the final-text check passed.

    About assessment marcus-reports-of-llms-mastering-math-have-552698-a1.