MathNet-Retrieve

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+---
+license: cc-by-4.0
+pretty_name: MathNet-Retrieve
+task_categories:
+  - text-retrieval
+language:
+  - multilingual
+tags:
+  - mathematics
+  - olympiad
+  - competition-math
+  - retrieval
+  - mteb
+  - beir
+  - arxiv:2604.18584
+size_categories:
+  - 100K<n<1M
+configs:
+  - config_name: easy-corpus
+    data_files:
+      - split: test
+        path: easy/corpus.jsonl
+  - config_name: easy-queries
+    data_files:
+      - split: test
+        path: easy/queries.jsonl
+  - config_name: easy-qrels
+    data_files:
+      - split: test
+        path: easy/qrels/test.jsonl
+  - config_name: medium-corpus
+    data_files:
+      - split: test
+        path: medium/corpus.jsonl
+  - config_name: medium-queries
+    data_files:
+      - split: test
+        path: medium/queries.jsonl
+  - config_name: medium-qrels
+    data_files:
+      - split: test
+        path: medium/qrels/test.jsonl
+  - config_name: hard-corpus
+    data_files:
+      - split: test
+        path: hard/corpus.jsonl
+  - config_name: hard-queries
+    data_files:
+      - split: test
+        path: hard/queries.jsonl
+  - config_name: hard-qrels
+    data_files:
+      - split: test
+        path: hard/qrels/test.jsonl
+---
+# MathNet-Retrieve
+The **Math-Aware Retrieval** benchmark from [MathNet](https://mathnet.mit.edu)
+([paper](https://arxiv.org/abs/2604.18584)): given an Olympiad problem, retrieve a
+*mathematically equivalent* reformulation of it from a large corpus of hard
+negatives. Each equivalent is provided at three difficulty tiers — `easy`,
+`medium`, `hard` — that increasingly disguise the surface form while preserving the
+underlying problem, so retrieval gets sharply harder even though the answer is the same.
+- **15,000** queries, one gold-equivalent target per tier.
+- **3 tiers** (`easy`, `medium`, `hard`), over a shared **117,088-doc** corpus (equivalents at all
+  tiers + LLM near-miss hard negatives + distractors).
+- Standard **BEIR / MTEB** layout.
+## Quick start
+```python
+from datasets import load_dataset
+tier = "hard"  # one of "easy", "medium", "hard"
+corpus  = load_dataset("ShadenA/MathNet-Retrieve", f"{tier}-corpus", split="test")   # {_id, text}
+queries = load_dataset("ShadenA/MathNet-Retrieve", f"{tier}-queries", split="test")  # {_id, text}
+qrels   = load_dataset("ShadenA/MathNet-Retrieve", f"{tier}-qrels", split="test")    # {query-id, corpus-id, score}
+```
+All three configs share the same corpus and queries; they differ only in which
+reformulation is the gold positive. Each query has exactly **one** positive, and the
+query's own equivalents at the other tiers act as negatives.
+## Tiers
+| Tier | Queries | Corpus | What changes |
+|---|---|---|---|
+| easy | 15,000 | 117,088 | light paraphrase (renamed vars, reordered clauses) |
+| medium | 15,000 | 117,088 | moderate restructuring (notation, framing) |
+| hard | 15,000 | 117,088 | heavy disguise; minimal lexical overlap |
+## Results
+Recall@k (%) on the shared corpus; **bold** = best.
+| Model | Easy R@1 | Easy R@5 | Medium R@1 | Medium R@5 | Hard R@1 | Hard R@5 |
+|---|---|---|---|---|---|---|
+| `gemini-embedding-001` | 11.4 | **90.7** | 2.5 | **77.1** | 0.0 | **9.3** |
+| `qwen3-embedding-4B` | **14.8** | 83.5 | **2.9** | 71.6 | 0.0 | 8.4 |
+| `text-embedding-3-large` | 6.8 | 78.1 | 1.6 | 68.3 | 0.0 | 2.6 |
+| `all-mpnet-base-v2` | 6.8 | 82.5 | 1.3 | 51.0 | 0.0 | 1.2 |
+| `multi-qa-mpnet-base-dot-v1` | 5.1 | 80.6 | 1.1 | 47.3 | 0.0 | 0.8 |
+| `text-embedding-ada-002` | 3.3 | 66.1 | 1.1 | 55.4 | 0.0 | 0.7 |
+| `text-embedding-3-small` | 3.6 | 59.8 | 1.4 | 51.5 | 0.0 | 0.5 |
+Every model degrades sharply from easy to hard: even the strongest solve `easy` but
+collapse on `hard` (Recall@1 ≈ 0; hard Recall@10 tops out near 54%). Retrieving
+equivalent problems remains unreliable once the surface form is disguised.
+## Caveats
+- **Not contamination-clean** — source problems may appear in pretraining data; prefer relative comparisons.
+- Equivalents and hard negatives are **LLM-generated and machine-verified**; a small fraction may be imperfect.
+## License & citation
+Released under **CC BY 4.0**, consistent with [`ShadenA/MathNet`](https://huggingface.co/datasets/ShadenA/MathNet).
+```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}
+}
+```
+[Project site](https://mathnet.mit.edu) · [Paper](https://arxiv.org/abs/2604.18584) · [Main dataset](https://huggingface.co/datasets/ShadenA/MathNet)

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