2026-07-08 08:57:50 +00:00

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# ArXiv-Math
## Overview
ArXiv-Math is a benchmark of 103 research-level mathematics problems extracted from arXiv preprints. These problems represent cutting-edge mathematical research and test the ability of language models to reason about advanced mathematical concepts at the frontier of knowledge.
## Task Description
- **Task Type**: Research-Level Mathematics Problem Solving
- **Input**: Advanced mathematical problem from arXiv papers
- **Output**: Step-by-step solution with final answer
- **Difficulty**: Research / graduate level
## Key Features
- 103 problems sourced from arXiv preprints (December 2024 - March 2025)
- Four monthly subsets: december, february, january, march
- Covers diverse areas: algebra, combinatorics, analysis, geometry, number theory
- Problems require deep mathematical reasoning and domain expertise
- Represents the frontier of mathematical research difficulty
## Evaluation Notes
- Default configuration uses **0-shot** evaluation
- Answers should be formatted within `\boxed{}` for proper extraction
- Numeric accuracy metric with symbolic equivalence checking
- Results can be broken down by monthly competition subset
## Properties
| Property | Value |
|----------|-------|
| **Benchmark Name** | `arxivmath` |
| **Dataset ID** | [evalscope/arxivmath](https://modelscope.cn/datasets/evalscope/arxivmath/summary) |
| **Paper** | N/A |
| **Tags** | `Math`, `Reasoning` |
| **Metrics** | `acc` |
| **Default Shots** | 0-shot |
| **Evaluation Split** | `train` |
## Data Statistics
| Metric | Value |
|--------|-------|
| Total Samples | 103 |
| Prompt Length (Mean) | 622.88 chars |
| Prompt Length (Min/Max) | 224 / 1392 chars |
**Per-Subset Statistics:**
| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |
|--------|---------|-------------|------------|------------|
| `arxiv/december` | 17 | 720.88 | 256 | 1392 |
| `arxiv/february` | 32 | 573.78 | 269 | 1147 |
| `arxiv/january` | 23 | 711.17 | 325 | 1270 |
| `arxiv/march` | 31 | 554.32 | 224 | 1213 |
## Sample Example
**Subset**: `arxiv/december`
```json
{
"input": [
{
"id": "c7cbf85d",
"content": "Problem:\nLet $k$ be a field, let $V$ be a $k$-vector space of dimension $d$, and let $G\\subseteq GL(V)$ be a finite group. Set $r:=\\dim_k (V^*)^G$ and assume $r\\ge 1$. Let $R:=k[V]^G$ be the invariant ring, and write its Hilbert quasi-polynom ... [TRUNCATED 71 chars] ... {d-2}+\\cdots+a_1(n)n+a_0(n),\n\\]\nwhere each $a_i(n)$ is a periodic function of $n$. Compute the sum of the indices $i\\in\\{0,1,\\dots,d-1\\}$ for which $a_i(n)$ is constant.\n\nPlease reason step by step, and put your final answer within \\boxed{}.\n"
}
],
"target": "\\frac{r(2d-r-1)}{2}",
"id": 0,
"group_id": 0,
"subset_key": "arxiv/december",
"metadata": {
"problem_idx": 1,
"problem_type": [
""
],
"source": 2512.00811
}
}
```
## Prompt Template
**Prompt Template:**
```text
Problem:
{question}
Please reason step by step, and put your final answer within \boxed{{}}.
```
## Usage
### Using CLI
```bash
evalscope eval \
--model YOUR_MODEL \
--api-url OPENAI_API_COMPAT_URL \
--api-key EMPTY_TOKEN \
--datasets arxivmath \
--limit 10 # Remove this line for formal evaluation
```
### Using Python
```python
from evalscope import run_task
from evalscope.config import TaskConfig
task_cfg = TaskConfig(
model='YOUR_MODEL',
api_url='OPENAI_API_COMPAT_URL',
api_key='EMPTY_TOKEN',
datasets=['arxivmath'],
dataset_args={
'arxivmath': {
# subset_list: ['arxiv/december', 'arxiv/february', 'arxiv/january'] # optional, evaluate specific subsets
}
},
limit=10, # Remove this line for formal evaluation
)
run_task(task_cfg=task_cfg)
```