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+---
+pretty_name: MathNet — Olympiad Math Reasoning & Retrieval (Preview)
+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
+  - preview
+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_Republic
+    data_files:
+      - split: train
+        path: data/Czech_Republic/train-*.parquet
+  - config_name: Czech-Polish-Slovak_Mathematical_Match
+    data_files:
+      - split: train
+        path: data/Czech-Polish-Slovak_Mathematical_Match/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: 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_dark.png" alt="MathNet" width="960"/>
+
+<a href="https://mathnet.mit.edu"><img alt="Website" src="https://img.shields.io/badge/website-mathnet.mit.edu-0d056f"></a>
+<a href="https://arxiv.org/abs/2604.18584"><img alt="ICLR 2026" src="https://img.shields.io/badge/ICLR-2026-b31b1b"></a>
+
+**Shaden Alshammari**<sup>1\*</sup> &ensp; **Kevin Wen**<sup>1\*</sup> &ensp; **Abrar Zainal**<sup>3\*</sup> &ensp; **Mark Hamilton**<sup>1</sup>
+**Navid Safaei**<sup>4</sup> &ensp; **Sultan Albarakati**<sup>2</sup> &ensp; **William T. Freeman**<sup>1†</sup> &ensp; **Antonio Torralba**<sup>1†</sup>
+
+<sup>1</sup>MIT &ensp; <sup>2</sup>KAUST &ensp; <sup>3</sup>HUMAIN &ensp; <sup>4</sup>Bulgarian Academy of Sciences &ensp; <sub>\*† equal contribution</sub>
+
+</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)
+
+---
+## 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["year"], row["country"])
+print(row["problem_markdown"])
+for img in row["images"]:
+    img.show()  # PIL image — renders inline in the HF viewer
+```
+
+## Overview
+
+<img src="assets/overview.png" alt="MathNet overview: large-scale multilingual data, high-quality solutions, diverse topics, and three evaluation tasks" width="100%"/>
+
+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, page numbers, authors where given) is preserved on every problem.
+
+## What this preview contains
+
+A **diverse 100-problem slice** sampled round-robin across countries, prioritizing problems with figures so the multimodal path is visible end-to-end. Images are **embedded** in the parquet as HF `Image()` features — they render inline in the dataset viewer and decode to PIL on load.
+
+## Schema
+
+| Column | Type | Notes |
+|---|---|---|
+| `unique_id` | string | Stable SHA-256 content hash |
+| `country` | string | Country / regional body of origin |
+| `competition` | string | e.g. `IMO 2023`, `Cono Sur Mathematical Olympiad` |
+| `year` | int32 | Year of competition |
+| `section` | string\|null | Day / round / level |
+| `problem_number` | string | As printed in the booklet |
+| `problem_markdown` | string | Problem statement (Markdown + LaTeX) |
+| `solutions_markdown` | list&lt;string&gt; | Official / provided solutions |
+| `answers_markdown` | list&lt;string&gt; | Final answers when stated separately |
+| `topics` | list&lt;list&lt;string&gt;&gt; | Hierarchical tags |
+| `topics_flat` | list&lt;string&gt; | Joined `A > B > C` strings |
+| `language` | string | Source booklet language |
+| `source_booklet` | string | Booklet id (e.g. `ARG_2003`) |
+| `booklet_source` | string | Upstream collection label |
+| `has_images` | bool | Whether the problem cites figures |
+| `num_images` | int32 | Count of referenced figures |
+| `images` | list&lt;Image&gt; | Inlined bytes, decoded to PIL |
+| `natural_language_description` | string\|null | LLM-assisted NL rephrasing |
+| `main_ideas` | list&lt;string&gt; | LLM-assisted key solution ideas |
+| `final_answer` | string\|null | LLM-extracted final answer |
+| `problem_type` | string\|null | `proof`, `answer`, `proof and answer`, … |
+| `metadata_confidence` | float32 | Self-rated confidence of LLM metadata |
+| `original_problem_markdown` | string\|null | Pre-normalization text |
+
+> The enriched fields (`natural_language_description`, `main_ideas`, `final_answer`, `problem_type`, `metadata_confidence`) are **LLM-assisted** and not fully human-audited in the preview. Treat them as convenience annotations, not ground truth.
+
+## Configs / splits
+
+One config per **country or regional body** plus a default `all` config unioning everything. Each config has a single `train` split — this is a preview, not the train/test partitioning of `MathNet-Solve` (which is `train: 23,776`, `test: 6,400`, `test-hard: 500` in the full 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.
+- **Preview schema may change** before the full release.
+- **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 `source_booklet` 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>
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