132 lines
3.7 KiB
Markdown
132 lines
3.7 KiB
Markdown
# ArXiv-Math
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## Overview
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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.
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## Task Description
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- **Task Type**: Research-Level Mathematics Problem Solving
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- **Input**: Advanced mathematical problem from arXiv papers
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- **Output**: Step-by-step solution with final answer
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- **Difficulty**: Research / graduate level
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## Key Features
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- 103 problems sourced from arXiv preprints (December 2024 - March 2025)
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- Four monthly subsets: december, february, january, march
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- Covers diverse areas: algebra, combinatorics, analysis, geometry, number theory
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- Problems require deep mathematical reasoning and domain expertise
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- Represents the frontier of mathematical research difficulty
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## Evaluation Notes
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- Default configuration uses **0-shot** evaluation
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- Answers should be formatted within `\boxed{}` for proper extraction
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- Numeric accuracy metric with symbolic equivalence checking
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- Results can be broken down by monthly competition subset
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## Properties
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| Property | Value |
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|----------|-------|
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| **Benchmark Name** | `arxivmath` |
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| **Dataset ID** | [evalscope/arxivmath](https://modelscope.cn/datasets/evalscope/arxivmath/summary) |
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| **Paper** | N/A |
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| **Tags** | `Math`, `Reasoning` |
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| **Metrics** | `acc` |
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| **Default Shots** | 0-shot |
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| **Evaluation Split** | `train` |
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## Data Statistics
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| Metric | Value |
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|--------|-------|
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| Total Samples | 103 |
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| Prompt Length (Mean) | 622.88 chars |
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| Prompt Length (Min/Max) | 224 / 1392 chars |
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**Per-Subset Statistics:**
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| Subset | Samples | Prompt Mean | Prompt Min | Prompt Max |
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|--------|---------|-------------|------------|------------|
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| `arxiv/december` | 17 | 720.88 | 256 | 1392 |
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| `arxiv/february` | 32 | 573.78 | 269 | 1147 |
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| `arxiv/january` | 23 | 711.17 | 325 | 1270 |
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| `arxiv/march` | 31 | 554.32 | 224 | 1213 |
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## Sample Example
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**Subset**: `arxiv/december`
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```json
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{
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"input": [
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{
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"id": "c7cbf85d",
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"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"
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}
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],
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"target": "\\frac{r(2d-r-1)}{2}",
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"id": 0,
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"group_id": 0,
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"subset_key": "arxiv/december",
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"metadata": {
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"problem_idx": 1,
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"problem_type": [
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""
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],
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"source": 2512.00811
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}
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}
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```
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## Prompt Template
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**Prompt Template:**
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```text
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Problem:
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{question}
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Please reason step by step, and put your final answer within \boxed{{}}.
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```
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## Usage
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### Using CLI
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```bash
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evalscope eval \
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--model YOUR_MODEL \
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--api-url OPENAI_API_COMPAT_URL \
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--api-key EMPTY_TOKEN \
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--datasets arxivmath \
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--limit 10 # Remove this line for formal evaluation
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```
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### Using Python
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```python
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from evalscope import run_task
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from evalscope.config import TaskConfig
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task_cfg = TaskConfig(
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model='YOUR_MODEL',
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api_url='OPENAI_API_COMPAT_URL',
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api_key='EMPTY_TOKEN',
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datasets=['arxivmath'],
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dataset_args={
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'arxivmath': {
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# subset_list: ['arxiv/december', 'arxiv/february', 'arxiv/january'] # optional, evaluate specific subsets
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}
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},
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limit=10, # Remove this line for formal evaluation
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)
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run_task(task_cfg=task_cfg)
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```
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