Materialfigbench Eval

---
name: materialfigbench-eval
description: Evaluates multimodal large language models' ability to solve college-level materials science problems that require accurate visual interpretation of scientific figures, such as phase diagrams and stress-strain curves, alongside domain-specific textual reasoning. Use when the user wants to benchmark on MaterialFigBENCH, or asks about evaluating this task. Reports accuracy.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2603.11414
  bibtex_key: [REDACTED]
  confidence: high
---

# materialfigbench-eval

> MaterialFigBENCH: benchmark dataset with figures for evaluating college-level materials science problem-solving abilities of multimodal large language models — Yoshitake et al. (2026) (arXiv:2603.11414, 2026)

## What this evaluates

Evaluates multimodal large language models' ability to solve college-level materials science problems that require accurate visual interpretation of scientific figures, such as phase diagrams and stress-strain curves, alongside domain-specific textual reasoning.

## Datasets

- **MaterialFigBENCH** — total 137; splits: test (137)

## Metrics

- `accuracy` **(primary)** — range: [0, 1]
  - Calculated as the number of correct responses divided by the number of sampled responses per problem, then averaged across all 137 problems. A response is considered correct if it falls within the expert-defined answer range.

## Input / output format

**Input**: A textual problem statement accompanied by a scientific figure (e.g., phase diagram, stress-strain curve, Arrhenius plot) that is essential for solving the problem.

**Output**: Free-response numerical or textual answer. Models must provide values with appropriate significant digits to match expert-defined answer ranges.

## Scoring recipe

```python
total_correct = 0
total_samples = 0
for problem in dataset:
    samples = model.generate(problem.text, problem.figure)
    for sample in samples:
        total_samples += 1
        if is_within_expert_range(sample.answer, problem.answer_range):
            total_correct += 1
accuracy = total_correct / total_samples
```

## Common pitfalls

- Models often ignore the provided figures and rely on memorized domain knowledge (e.g., assuming a hypothetical element is carbon and using memorized solubility limits).
- Significant digit handling critically affects correctness; answers outside the expert-defined range due to rounding or sig-fig differences are marked incorrect.
- API and ChatGPT interfaces for the same model version can yield different accuracy scores due to sampling differences and interface-specific behaviors.

## Evidence (verbatim from paper)

> To obtain an accuracy value, we divided the number of correct responses by the number of sampled responses (10 for GPT-4o and 8 for GPT-o1 and GPT-5) and then averaged over all 137 problems.

## Citation

```bibtex
@misc{yoshitake2026materialfigbench,
  title={MaterialFigBENCH: benchmark dataset with figures for evaluating college-level materials science problem-solving abilities of multimodal large language models},
  author={Yoshitake et al. (2026)},
  year={2026},
  note={arXiv:2603.11414}
}
```

- arXiv: 2603.11414