README.md

---
pretty_name: "MathNet v0 — Olympiad Math Reasoning & Retrieval"
license: cc-by-4.0
task_categories:
  - question-answering
  - text-generation
  - image-to-text
language:
  - en
  - pt
  - es
  - fr
  - it
  - sr
  - sl
  - de
  - zh
  - ro
  - ko
  - nl
  - ru
  - mn
  - mk
  - pl
  - hu
tags:
  - mathematics
  - olympiad
  - reasoning
  - competition-math
  - multimodal
  - retrieval
  - iclr-2026
  - v0
size_categories:
  - 10K<n<100K
configs:
  - config_name: all
    data_files:
      - split: train
        path: data/all/train-*.parquet
    default: true
  - config_name: Argentina
    data_files:
      - split: train
        path: data/Argentina/train-*.parquet
  - config_name: Asia_Pacific_Mathematics_Olympiad_APMO
    data_files:
      - split: train
        path: data/Asia_Pacific_Mathematics_Olympiad_APMO/train-*.parquet
  - config_name: Austria
    data_files:
      - split: train
        path: data/Austria/train-*.parquet
  - config_name: Balkan_Mathematical_Olympiad
    data_files:
      - split: train
        path: data/Balkan_Mathematical_Olympiad/train-*.parquet
  - config_name: Baltic_Way
    data_files:
      - split: train
        path: data/Baltic_Way/train-*.parquet
  - config_name: Belarus
    data_files:
      - split: train
        path: data/Belarus/train-*.parquet
  - config_name: Benelux_Mathematical_Olympiad
    data_files:
      - split: train
        path: data/Benelux_Mathematical_Olympiad/train-*.parquet
  - config_name: Brazil
    data_files:
      - split: train
        path: data/Brazil/train-*.parquet
  - config_name: Bulgaria
    data_files:
      - split: train
        path: data/Bulgaria/train-*.parquet
  - config_name: Canada
    data_files:
      - split: train
        path: data/Canada/train-*.parquet
  - config_name: China
    data_files:
      - split: train
        path: data/China/train-*.parquet
  - config_name: Croatia
    data_files:
      - split: train
        path: data/Croatia/train-*.parquet
  - config_name: Czech-Polish-Slovak_Mathematical_Match
    data_files:
      - split: train
        path: data/Czech-Polish-Slovak_Mathematical_Match/train-*.parquet
  - config_name: Czech_Republic
    data_files:
      - split: train
        path: data/Czech_Republic/train-*.parquet
  - config_name: Estonia
    data_files:
      - split: train
        path: data/Estonia/train-*.parquet
  - config_name: European_Girls'_Mathematical_Olympiad_EGMO
    data_files:
      - split: train
        path: data/European_Girls'_Mathematical_Olympiad_EGMO/train-*.parquet
  - config_name: France
    data_files:
      - split: train
        path: data/France/train-*.parquet
  - config_name: Germany
    data_files:
      - split: train
        path: data/Germany/train-*.parquet
  - config_name: Greece
    data_files:
      - split: train
        path: data/Greece/train-*.parquet
  - config_name: Hong_Kong
    data_files:
      - split: train
        path: data/Hong_Kong/train-*.parquet
  - config_name: IMO
    data_files:
      - split: train
        path: data/IMO/train-*.parquet
  - config_name: Ibero-American_Mathematical_Olympiad
    data_files:
      - split: train
        path: data/Ibero-American_Mathematical_Olympiad/train-*.parquet
  - config_name: India
    data_files:
      - split: train
        path: data/India/train-*.parquet
  - config_name: Iran
    data_files:
      - split: train
        path: data/Iran/train-*.parquet
  - config_name: Ireland
    data_files:
      - split: train
        path: data/Ireland/train-*.parquet
  - config_name: Italy
    data_files:
      - split: train
        path: data/Italy/train-*.parquet
  - config_name: JBMO
    data_files:
      - split: train
        path: data/JBMO/train-*.parquet
  - config_name: Japan
    data_files:
      - split: train
        path: data/Japan/train-*.parquet
  - config_name: Mexico
    data_files:
      - split: train
        path: data/Mexico/train-*.parquet
  - config_name: Middle_European_Mathematical_Olympiad_MEMO
    data_files:
      - split: train
        path: data/Middle_European_Mathematical_Olympiad_MEMO/train-*.parquet
  - config_name: Moldova
    data_files:
      - split: train
        path: data/Moldova/train-*.parquet
  - config_name: Mongolia
    data_files:
      - split: train
        path: data/Mongolia/train-*.parquet
  - config_name: Netherlands
    data_files:
      - split: train
        path: data/Netherlands/train-*.parquet
  - config_name: New_Zealand
    data_files:
      - split: train
        path: data/New_Zealand/train-*.parquet
  - config_name: Nordic_Mathematical_Olympiad
    data_files:
      - split: train
        path: data/Nordic_Mathematical_Olympiad/train-*.parquet
  - config_name: North_Macedonia
    data_files:
      - split: train
        path: data/North_Macedonia/train-*.parquet
  - config_name: Philippines
    data_files:
      - split: train
        path: data/Philippines/train-*.parquet
  - config_name: Portuguese_Language_Countries_Olympiad_OMCPLP
    data_files:
      - split: train
        path: data/Portuguese_Language_Countries_Olympiad_OMCPLP/train-*.parquet
  - config_name: Romania
    data_files:
      - split: train
        path: data/Romania/train-*.parquet
  - config_name: Romanian_Master_of_Mathematics_RMM
    data_files:
      - split: train
        path: data/Romanian_Master_of_Mathematics_RMM/train-*.parquet
  - config_name: Russia
    data_files:
      - split: train
        path: data/Russia/train-*.parquet
  - config_name: Saudi_Arabia
    data_files:
      - split: train
        path: data/Saudi_Arabia/train-*.parquet
  - config_name: Serbia
    data_files:
      - split: train
        path: data/Serbia/train-*.parquet
  - config_name: Silk_Road_Mathematics_Competition
    data_files:
      - split: train
        path: data/Silk_Road_Mathematics_Competition/train-*.parquet
  - config_name: Singapore
    data_files:
      - split: train
        path: data/Singapore/train-*.parquet
  - config_name: Slovenia
    data_files:
      - split: train
        path: data/Slovenia/train-*.parquet
  - config_name: South_Africa
    data_files:
      - split: train
        path: data/South_Africa/train-*.parquet
  - config_name: South_Korea
    data_files:
      - split: train
        path: data/South_Korea/train-*.parquet
  - config_name: Soviet_Union
    data_files:
      - split: train
        path: data/Soviet_Union/train-*.parquet
  - config_name: Spain
    data_files:
      - split: train
        path: data/Spain/train-*.parquet
  - config_name: Switzerland
    data_files:
      - split: train
        path: data/Switzerland/train-*.parquet
  - config_name: Taiwan
    data_files:
      - split: train
        path: data/Taiwan/train-*.parquet
  - config_name: Thailand
    data_files:
      - split: train
        path: data/Thailand/train-*.parquet
  - config_name: Turkey
    data_files:
      - split: train
        path: data/Turkey/train-*.parquet
  - config_name: Ukraine
    data_files:
      - split: train
        path: data/Ukraine/train-*.parquet
  - config_name: United_States
    data_files:
      - split: train
        path: data/United_States/train-*.parquet
  - config_name: Vietnam
    data_files:
      - split: train
        path: data/Vietnam/train-*.parquet
  - config_name: Zhautykov_Olympiad
    data_files:
      - split: train
        path: data/Zhautykov_Olympiad/train-*.parquet
---

<div align="center">
<img src="assets/title_w_logo_light.png" alt="MathNet" width="960"/>
<img src="assets/overview.png" alt="MathNet overview: large-scale multilingual data, high-quality solutions, diverse topics, and three evaluation tasks" width="100%"/>
  <a href="https://arxiv.org/abs/2604.18584"><img alt="ICLR 2026" src="https://img.shields.io/badge/ICLR-2026-b31b1b"></a>
  <a href="https://mathnet.mit.edu"><img alt="Website" src="https://img.shields.io/badge/website-mathnet.mit.edu-0d056f"></a>
</div>

[Quick Start](#quick-start) · [Overview](#overview) · [Tasks](#three-benchmark-tasks) · [Comparison](#how-mathnet-compares-to-existing-math-benchmarks) · [Dataset Stats](#dataset-at-a-glance) · [Data Sources](#data-sources) · [Pipeline](#data-pipeline) · [Schema](#schema) · [License](#license) · [Citation](#citation) 

> **This is the official MathNet v0.** A larger version **v1** will be uploaded by **Friday, April 24, 2026** (more countires, problems and richer metadata). Schema is stable but field values may be revised in v1.
 
---
## Quick start

```python
from datasets import load_dataset

# Default: all problems
ds = load_dataset("ShadenA/MathNet", split="train")

# Or a specific country / competition-body config
arg  = load_dataset("ShadenA/MathNet", "Argentina", split="train")
apmo = load_dataset("ShadenA/MathNet", "Asia_Pacific_Mathematics_Olympiad_APMO", split="train")

row = ds[0]
print(row["competition"], row["country"])
print(row["problem_markdown"])
for img in row["images"]:
    img.show()  # PIL image — renders inline in the HF viewer
```

## Overview
Mathematical problem solving remains a challenging test of reasoning for large language and multimodal models, yet existing benchmarks are limited in size, language coverage, and task diversity. We introduce **MathNet**, a high-quality, large-scale, multimodal, and multilingual dataset of Olympiad-level math problems together with a benchmark for evaluating mathematical reasoning in generative models **and** mathematical retrieval in embedding-based systems.

MathNet spans **47 countries**, **17 languages**, and **two decades** of competitions, comprising **30,676 expert-authored problems with solutions** across diverse domains. Alongside the core dataset, we construct a retrieval benchmark of mathematically equivalent and structurally similar problem pairs curated by human experts.

---

## Three benchmark tasks

| | Task | What it measures |
|---|---|---|
| **I** | **Problem Solving** | Generative models on Olympiad problems, graded against expert solutions |
| **II** | **Math-Aware Retrieval** | Embedding models' ability to retrieve mathematically equivalent / structurally similar problems |
| **III** | **Retrieval-Augmented Problem Solving** | How retrieval quality affects reasoning when similar problems are given as context |

Even state-of-the-art reasoners remain challenged: **78.4% (Gemini-3.1-Pro)** and **69.3% (GPT-5)** on `MathNet-Solve-Test`. Embedding models struggle with equivalence retrieval (Recall@1 under 5% for all tested models), and RAG gains are highly sensitive to retrieval quality — expert retrieval lifts DeepSeek-V3.2-Speciale to **97.3%** on `MathNet-RAG`.


## How MathNet compares to existing math benchmarks

| Benchmark | Size | Languages | Multimodal | Source | Difficulty |
|---|---:|---|:-:|---|---|
| GSM8K | 8,500 | EN | — | Crowdsourced | Grade school |
| MATH | 12,500 | EN | — | Competitions/textbooks | High school |
| MATH-Vision | 3,040 | EN | ✓ | Math competitions | High school |
| OlympiadBench | 6,142 | EN, ZH | ✓ | Official websites | Olympiad |
| OlympicArena | 3,233 | EN, ZH | ✓ | Official websites | Olympiad |
| Omni-Math | 4,428 | EN | — | AoPS / contest pages | Olympiad |
| OlymMATH | 200 | EN, ZH | — | AoPS / official | Olympiad |
| MathArena | 162 | EN | ✓ | Newly released competitions | Olympiad |
| IMOBench | 460 | EN | — | IMO & national archives | Olympiad |
| **MathNet (ours)** | **30,676** | **17** (EN, ZH, ES, RU, FR, RO, + 11 more) | **✓** | **Official country booklets / international & national contests** | **Olympiad** |

## Dataset at a glance

<img src="assets/dataset_stats.png" alt="MathNet dataset statistics: contest types, solution length vs. prior benchmarks, problems per year, topic distribution, and language distribution" width="100%"/>

**What the figure shows.** *(a)* A mix of national, regional, TST, and international competitions. *(b)* MathNet solutions are **substantially longer** than those in prior math benchmarks — long-form proofs, not one-line answers. *(c)* Problems per year — the corpus has grown steadily since the early 2000s. *(d)* Coverage across geometry, algebra, combinatorics, number theory, and their sub-topics. *(e)* **74% English, 26% non-English** across **17 languages**; Portuguese, Spanish, French, Italian, Serbian, Slovenian, German, Chinese, Romanian, Korean, Dutch, Russian, Mongolian, Macedonian, Polish, and Hungarian all appear.

### Topic taxonomy (excerpt)

MathNet ships with a curated olympiad-style taxonomy. Top-level domains include:

- **Geometry** — plane (triangles, quadrilaterals, circles, concurrency/collinearity, transformations, Miquel/Simson/Brocard, geometric inequalities, combinatorial geometry, analytic methods), solid, differential, non-Euclidean
- **Algebra** — prealgebra, polynomials, inequalities, functional equations, sequences/series, linear algebra, abstract algebra
- **Number Theory** — divisibility, primes, modular arithmetic, Diophantine equations, quadratic residues, \(p\)-adic methods
- **Combinatorics** — counting, graph theory, extremal / pigeonhole, invariants/monovariants, games, coloring, generating functions
- **Calculus / Analysis** — limits, inequalities, real analysis, combinatorial analysis
- **Probability & Statistics** — discrete and continuous

Every problem carries a hierarchical topic path (e.g. `Geometry > Plane Geometry > Quadrilaterals > Cyclic quadrilaterals`) usable for stratified evaluation or curriculum construction.

## Data sources

Each year, participating IMO countries contribute original problems for use in their national contests and team selection examinations. MathNet is built from **official problem booklets** collected from **47 countries spanning 1985–2025** — **1,595 PDF volumes** totalling more than **25,000 pages**. Unlike prior math benchmarks that rely on community platforms such as AoPS, every problem and solution in MathNet is authored and disseminated by national teams themselves, ensuring expert-level quality, stylistic consistency, and immunity from the noisy or informal annotations that plague crowd-sourced collections.

A meaningful portion of the collection — particularly older national booklets — was physically obtained and scanned by hand by our IMO expert co-authors, who have attended the International Mathematical Olympiad since 2006 and accumulated a personal archive of official competition materials over nearly two decades.

## Data pipeline

<img src="assets/pipeline.png" alt="MathNet data extraction and curation pipeline" width="100%"/>

Extracting aligned problem–solution pairs from a heterogeneous corpus of mathematical documents is non-trivial: some booklets separate problems and solutions into different sections, others interleave them; numbering schemes and naming conventions vary across countries and even within a single document. Regex-based heuristics break down at this scale, so we designed a multi-stage LLM pipeline.

**Stage 1 — Document ingestion & segmentation.** All booklets are converted to Markdown via `dots-ocr`, a multilingual document parsing framework designed for both digital typeset PDFs and scanned copies across many languages. `Gemini-2.5-Flash` then identifies problem and solution segments by outputting only their line numbers, and records authors, hints, remarks, source file, and page numbers for provenance.

**Stage 2 — Problem–solution extraction.** Given the line segments from Stage 1, `GPT-4.1` extracts the corresponding problem and solution in LaTeX-friendly Markdown, together with a surrounding text buffer to handle cases where content spans across context boundaries.

**Stage 3 — Extraction verification.** Each extracted pair passes three independent checks before being retained:
1. **Rule-based similarity check** — text similarity between the extraction and original OCR output ensures the LLM made only formatting changes and introduced no hallucinated content.
2. **GPT-4.1-as-judge** — GPT-4.1 compares page screenshots against the extracted pair to catch OCR errors, incorrect figure associations, and incomplete solutions.
3. **Human expert review** — low-confidence cases are manually reviewed by annotators. A pair is retained only if all three mechanisms agree.

Provenance (source booklet, authors where given) is preserved on every problem.

## What v0 contains

This is the **v0 drop** of MathNet — the first complete public release:

- **27,817 problems** across **58 country / regional-body configs**
- **5,148 problems with figures**, totalling **7,541 images** embedded inline as HF `Image()` features (they render in the viewer and decode to PIL on load)
- All image references in problem and solution markdown are rewritten to the uniform `![](attached_image_N.png)` convention and the corresponding bytes ship in the `images` column in the same order
- Default `all` config opens with a curated head of ~120 country-diverse, figure-rich problems so the dataset viewer preview is visually representative; the remainder of `all` is shuffled

A more refined **v1** — larger, with improved extraction, deduplication, and metadata — will be uploaded by **Friday, April 24, 2026**.

## Schema

| Column | Type | Notes |
|---|---|---|
| `id` | string | Short 4-char base36 identifier, stable across rebuilds |
| `country` | string | Country or regional body of origin |
| `competition` | string | e.g. `IMO 2023`, `Cono Sur Mathematical Olympiad` |
| `problem_markdown` | string | Problem statement (Markdown + LaTeX) with `![](attached_image_N.png)` refs |
| `solutions_markdown` | list&lt;string&gt; | Official / provided solutions, one entry per solution |
| `topics_flat` | list&lt;string&gt; | Hierarchical topic paths joined as `A > B > C` |
| `language` | string | Source booklet language |
| `booklet_source` | string | Upstream collection label |
| `images` | list&lt;Image&gt; | Inlined figure bytes, decoded to PIL; positions align with `attached_image_N.png` refs in the markdown |
| `problem_type` | string\|null | `proof only`, `answer only`, `proof and answer` — LLM-assisted |
| `final_answer` | string\|null | LLM-extracted final answer for answer-bearing problems |

> `problem_type` and `final_answer` are **LLM-assisted** and not fully human-audited in v0. Treat them as convenience annotations, not ground truth.

## Configs / splits

One config per **country or regional body** (58 total) plus a default `all` config unioning everything. Each config has a single `train` split — this is the public v0 release, not the train/test partitioning of `MathNet-Solve` (which is `train: 23,776`, `test: 6,400`, `test-hard: 500` in the paper release).

## Intended uses & limitations

**Good for.** Olympiad-level reasoning evaluation, multilingual math evaluation, figure-grounded multimodal math, topic-stratified analysis, retrieval benchmarks over mathematical structure, and **RL training** — the large pool of expert-written solutions provides dense rewards for verifiable-answer problems, while the math-aware similarity pairs open a new axis: rewarding a model for retrieving a structurally equivalent problem is a natural, automatically verifiable signal that does not require a closed-form answer.

**Caveats.**
- **Not contamination-clean.** Olympiad problems are indexed widely; assume leakage when evaluating pretrained models.
- **v0 field values may be revised in v1** (improved extraction / dedup / metadata).
- **LLM-assisted metadata is imperfect.**

## License

With the kind support of IMO President Gregor Dolinar, we reached out to the leaders of all participating countries and obtained their permission to share this dataset publicly. Where a country or contest organization asserts its own copyright, that copyright is retained and takes precedence — see `competition`, `country`, and `booklet_source` on each row. For all remaining problems where no explicit copyright was asserted, the dataset is released under **[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)**.

In short: use freely, cite the paper, and respect any explicit rights claimed by the original national team.

If you are a rightsholder with a concern, please open an issue or email [shaden@mit.edu](mailto:shaden@mit.edu).

## Citation

```bibtex
@inproceedings{alshammari2026mathnet,
  title     = {MathNet: A Global Multimodal Benchmark for Mathematical
               Reasoning and Retrieval},
  author    = {Alshammari, Shaden and Wen, Kevin and Zainal, Abrar and
               Hamilton, Mark and Safaei, Navid and Albarakati, Sultan and
               Freeman, William T. and Torralba, Antonio},
  booktitle = {International Conference on Learning Representations},
  year      = {2026},
  url       = {https://mathnet.mit.edu}
}
```

## Links

- 🌐 **Website & paper:** <https://mathnet.mit.edu>
- 🔭 **Browse all 30K problems:** <https://mathnet.mit.edu/explorer.html>
- ✉️ **Contact:** [shaden@mit.edu](mailto:shaden@mit.edu)

<p align="center"><sub>© 2026 Massachusetts Institute of Technology · MathNet · ICLR 2026</sub></p>