introvoyz041/aops
AoPS: Art of Problem Solving Competition Mathematics Dataset Description This dataset is a collection of 80,661 competition mathematics problems and solutions obtained from the Art of Problem Solving (AoPS) community wiki and forums. It covers a wide range of mathematical contests and olympiads, including problems from events such as AIME, BAMO, IMO, and various national and memorial competitions. The dataset was curated by AI-MO (Project Numina), an initiative… See the full description on the dataset page: https://huggingface.co/datasets/introvoyz041/aops.
AoPS: Art of Problem Solving Competition Mathematics
Dataset Description
This dataset is a collection of 80,661 competition mathematics problems and solutions obtained from the Art of Problem Solving (AoPS) community wiki and forums. It covers a wide range of mathematical contests and olympiads, including problems from events such as AIME, BAMO, IMO, and various national and memorial competitions.
The dataset was curated by AI-MO (Project Numina), an initiative focused on building AI systems capable of mathematical reasoning at the olympiad level.
Dataset Structure
Fields
Metadata Fields
Splits
Example
{
"problem": "Let $ABC$ be an acute triangle with altitude $AD$ ($D \\in BC$). The line through $C$ parallel to $AB$ meets the perpendicular bisector of $AD$ at $G$. Show that $AC = BC$ if and only if $\\angle AGC = 90°$.",
"solution": "...",
"candidates": ["..."],
"tags": ["origin:aops", "2022 Contests", "2022 3rd Memorial \"Aleksandar Blazhevski-Cane\""],
"metadata": {
"answer_score": 130,
"boxed": false,
"end_of_proof": true,
"n_reply": 3,
"path": "Contest Collections/2022 Contests/2022 3rd Memorial .../2759376.json"
}
}Topic Coverage
Problems span a broad range of competition mathematics topics, including:
- Geometry -- triangle properties, cyclic quadrilaterals, angle chasing
- Number Theory -- divisibility, modular arithmetic, Diophantine equations
- Algebra -- inequalities, polynomials, functional equations
- Combinatorics -- counting, graph theory, board coloring problems
Usage
from datasets import load_dataset
dataset = load_dataset("AI-MO/aops")
# Access a problem
print(dataset["train"][0]["problem"])
print(dataset["train"][0]["solution"])Intended Use
- Training and evaluating mathematical reasoning models
- Benchmarking LLMs on competition-level mathematics
- Studying solution quality and problem difficulty distributions
- Building retrieval-augmented generation (RAG) systems for math tutoring
Source
All problems and solutions originate from the Art of Problem Solving community.
