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BAMBOO: Benchmark for Autonomous ML Build-and-Output Observation
A large-scale benchmark for evaluating AI agents' ability to reproduce ML research papers using the authors' original code.
Dataset Summary
| Metric | Value |
|---|---|
| Total papers | 6,148 |
| Papers with PDF | 5,495 (89%) |
| Papers with structured MD | 3,983 (64%) |
| Venues | ICML, ICLR, NeurIPS, CVPR, ICCV, ACL, EMNLP, AAAI, ICRA |
| Year | 2025 |
| Code coverage | 100% (all papers have verified code_url + code_commit) |
| Abstracts | 100% |
| Difficulty scores | 100% |
Files
bamboo_dataset.json— Full paper metadata (6,148 entries)paper_pdfs/— Original paper PDFs (5,495 files, ~32GB)paper_markdowns/— MinerU hybrid-auto-engine extracted markdown (3,983 files)
PDF Extraction
PDFs are extracted using MinerU v2.7.6 with the hybrid-auto-engine backend (highest quality VLM-based extraction). This preserves:
- Correct paragraph ordering
- Table structure as markdown
- Mathematical formulas
- Figure references
Venue Breakdown (papers with MD)
| Venue | Papers |
|---|---|
| ICML | 1,109 |
| ICLR | 669 |
| ICCV | 501 |
| CVPR | 408 |
| NeurIPS | 359 |
| ACL | 327 |
| EMNLP | 294 |
| AAAI | 275 |
| ICRA | 41 |
Usage
from huggingface_hub import hf_hub_download
import json
# Download metadata
path = hf_hub_download("xln3/bamboo-papers", "bamboo_dataset.json", repo_type="dataset")
papers = json.load(open(path))
# Filter papers with markdown
papers_with_md = [p for p in papers if p["has_md"]]
print(f"{len(papers_with_md)} papers with structured markdown")
# Download a specific paper's markdown
md_path = hf_hub_download("xln3/bamboo-papers", "paper_markdowns/bamboo-00001.md", repo_type="dataset")
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