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Datasets:
saidutta69
/
unsolved-math-clean

Tasks:
Question Answering
Text Generation
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
Tags:
mathematics
open-problems
research
benchmark
reasoning
latex
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
unsolved-math-clean
File size: 3,730 Bytes
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---
license: cc-by-4.0
language:
  - en
pretty_name: Unsolved Math Clean
tags:
  - mathematics
  - open-problems
  - research
  - benchmark
  - reasoning
  - latex
  - clean
task_categories:
  - question-answering
  - text-generation
size_categories:
  - 1K<n<10K
configs:
  - config_name: problems
    default: true
    data_files:
      - split: train
        path: data/problems/train.parquet
      - split: validation
        path: data/problems/validation.parquet
      - split: test
        path: data/problems/test.parquet
  - config_name: open_problems_eval
    data_files:
      - split: train
        path: data/open_problems_eval/train.parquet
  - config_name: research_prompts
    data_files:
      - split: train
        path: data/research_prompts/train.parquet
      - split: validation
        path: data/research_prompts/validation.parquet
      - split: test
        path: data/research_prompts/test.parquet
---

# 🧠 Unsolved Math — Clean

<div align="center">
  <img src="https://res.cloudinary.com/cmazqjs6/image/upload/racer_is_op_banner_branded_pu7zud.png" alt="RACER IS OP" width="100%">
</div>

<br>

**8,626 curated open research problems** in mathematics and CS — including 122 Millennium Prize Problems — deduplicated, schema-flattened, and packaged as proper parquet configs with an eval-only benchmark view.

> **A reasoning frontier dataset:** every problem here is *actually unsolved or partially solved* — ideal for honest capability probing instead of contaminated benchmarks.

Clean derivative of [ulamai/UnsolvedMath](https://huggingface.co/datasets/ulamai/UnsolvedMath) (8,785 problems). License unchanged: **CC-BY-4.0**.

## 🧹 Quality Pipeline

| Step | Removed | Reason |
|------|--------:|--------|
| Raw input | 8,785 | Nested JSON |
| **Duplicate titles** | 153 | Same problem listed twice |
| **Empty statements** | 6 | No problem body |
| **Final clean** | **8,626** | Flattened schema, deterministic splits |

## 📊 Composition

By status:

| Status | Problems |
|--------|---------:|
| Open | 4,354 |
| Partially solved | 3,580 |
| Solved | 692 |

By difficulty:

| Level | Problems |
|-------|---------:|
| L1: Tractable | 908 |
| L2: Intermediate | 516 |
| L3: Advanced | 6,143 |
| L4: Expert | 937 |
| L5: Millennium Prize | 122 |

## 📦 Configs

| Config | Rows | Description |
|--------|-----:|-------------|
| `problems` *(default)* | 8,626 | Full metadata: statement, background, status, difficulty, category, provenance |
| `open_problems_eval` | 4,354 | Open problems only — benchmark/eval view |
| `research_prompts` | 8,626 | Instruction-formatted: "analyze this open problem, discuss partial results and obstacles" |

## 🎯 Usage

```python
from datasets import load_dataset

ds = load_dataset("saidutta69/unsolved-math-clean", "problems", split="train")

# Honest eval: only truly open problems
ev = load_dataset("saidutta69/unsolved-math-clean", "open_problems_eval")

# Research-style SFT prompts
rp = load_dataset("saidutta69/unsolved-math-clean", "research_prompts")
print(rp["train"][0]["instruction"][:300])
```

## ⚠️ Notes

- These are **unsolved problems** — there are no reference answers. Use for capability probing, calibration of hedging behavior, and research discussion; not for accuracy scoring.
- LaTeX notation preserved verbatim.
- Millennium-level problems are intentionally near-impossible; expect models to fail honestly.

## 📜 Citation

```bibtex
@misc{unsolved-math-clean,
  author = {Sai Dutta Abhishek Dash (clean); original by ulamai},
  title = {Unsolved Math — Clean},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/saidutta69/unsolved-math-clean}}
}
```