Frontiermath Eval

---
name: frontiermath-eval
description: Evaluates advanced mathematical reasoning and experimental problem-solving. It tests whether models can iteratively write and execute Python code to verify hypotheses, refine strategies, and derive correct solutions to expert-level, unsolved math problems. Use when the user wants to benchmark on FrontierMath, or asks about evaluating this task. Reports accuracy.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2411.04872
  bibtex_key: [REDACTED]
  confidence: high
---

# frontiermath-eval

> FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI — Glazer et al. (2024) (arXiv:2411.04872, 2024)

## What this evaluates

Evaluates advanced mathematical reasoning and experimental problem-solving. It tests whether models can iteratively write and execute Python code to verify hypotheses, refine strategies, and derive correct solutions to expert-level, unsolved math problems.

## Datasets

- **FrontierMath** — total ?; splits: test (-1)

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - Proportion of problems for which the model submits a correctly formatted final answer that matches the ground truth. Calculated as (number of solved problems) / (total problems). Reported as mean accuracy across multiple runs.

## Input / output format

**Input**: A mathematical problem statement requiring advanced reasoning.

**Output**: A self-contained Python code block containing the final answer, preceded by the exact marker comment `# This is the final answer`, and saved using Python's pickle module.

## Scoring recipe

```python
def score(predictions, golds):
    correct = 0
    total = len(predictions)
    for pred, gold in zip(predictions, golds):
        if pred is None:
            continue
        if '# This is the final answer' not in pred:
            continue
        if extract_final_answer(pred) == gold:
            correct += 1
    return correct / total if total > 0 else 0
```

## Common pitfalls

- Low overall success rate (<2%) means model rankings are highly unstable and sensitive to single successes.
- A correct final answer does not guarantee valid mathematical reasoning, as some problems can be solved via simulation or guessing without deeper understanding.
- Models forced by the 10,000-token limit may submit incorrect or unformatted answers, skewing accuracy if not handled uniformly.

## Evidence (verbatim from paper)

> Figure 6: Performance of leading language models on FrontierMath based on mean accuracy across 8 runs. All models show consistently poor performance, with even the best models solving less than 2% of problems in each run on average.

## Citation

```bibtex
@misc{glazer2024frontiermath,
  title={FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI},
  author={Glazer et al. (2024)},
  year={2024},
  note={arXiv:2411.04872}
}
```

- arXiv: 2411.04872