englund/quantum-lean-hard-vvuq-dataset
Quantum Lean Hard VVUQ Dataset Overview This dataset contains challenging Lean 4 verification and repair problems designed to test advanced reasoning capabilities and demonstrate the effectiveness of Physics World Models (PWMs) in formal verification. Problem Categories Type Theory: 3 problems (coercion, dependent types, universes) Tactic Sequences: 3 problems (wrong tactics, incorrect ordering) Proof Strategies: 3 problems (wrong high-level… See the full description on the dataset page: https://huggingface.co/datasets/englund/quantum-lean-hard-vvuq-dataset.
Quantum Lean Hard VVUQ Dataset
Overview
This dataset contains challenging Lean 4 verification and repair problems designed to test advanced reasoning capabilities and demonstrate the effectiveness of Physics World Models (PWMs) in formal verification.
Problem Categories
- Type Theory: 3 problems (coercion, dependent types, universes)
- Tactic Sequences: 3 problems (wrong tactics, incorrect ordering)
- Proof Strategies: 3 problems (wrong high-level approaches)
- Advanced Type Theory: 1 problems (higher-order reasoning)
- Baseline: 1 problems (simple for comparison)
Difficulty Distribution
- Easy: 1 problems
- Hard: 4 problems
- Expert: 6 problems
PWM Knowledge Levels
- W1 (Basic): 1 problems
- W2 (Intermediate): 3 problems
- W3 (Advanced): 7 problems
Expected Performance
- Method A (LLM only): Should struggle with hard/expert problems
- Method E (LLM + W3): Should handle most problems successfully
- Clear gradient: Performance should improve from A→B→C→D→E
Dataset Structure
Each example contains:
id: Unique problem identifiercategory: Problem category (typetheory, tacticsequence, proof_strategy, etc.)difficulty: Difficulty level (easy, hard, expert)corrupted_code: Lean code with intentional errorsrepair_target: Correct version of the codeerror_info: Detailed error informationdomain_knowledge_required: List of concepts needed to solvepwm_level: Required Physics World Model knowledge level
Usage
This dataset is designed for NeurIPS experiments demonstrating PWM effectiveness in formal verification tasks.
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("englund/quantum-lean-hard-vvuq-dataset")
# Access train split
train_data = dataset["train"]
# Example usage
for example in train_data:
print(f"Problem: {example['id']}")
print(f"Corrupted: {example['corrupted_code']}")
print(f"Target: {example['repair_target']}")
print(f"Difficulty: {example['difficulty']}")Requirements
- Lean 4 with Mathlib
- Enhanced diagnostics:
set_option diagnostics true - Actual compilation verification (RequirementLEAN)
Citation
If you use this dataset, please cite:
@dataset{englund2025quantum,
title={Quantum Lean Hard VVUQ Dataset},
author={Englund, Dirk},
year={2025},
url={https://huggingface.co/datasets/englund/quantum-lean-hard-vvuq-dataset}
}