SPaRC: A Spatial Pathfinding Reasoning Challenge

A comprehensive toolkit for spatial reasoning puzzle solving and model evaluation

Overview

SPaRC provides a comprehensive framework for evaluating language models on spatial reasoning tasks inspired by "The Witness" puzzle game. This package includes tools for dataset processing, solution validation, and model evaluation with beautiful terminal output.

Installation

Install the package from PyPI:

pip install sparc-puzzle

Or install from source:

git clone https://github.com/lkaesberg/SPaRC.git
cd SPaRC
pip install -e .

Quick Start

1. Testing a Model on the Dataset

Run the complete benchmark on your model:

sparc --api-key "your-openai-api-key" --model "gpt-4" --batch-size 5

Key Features:

• 🔄 Resume Support: Automatically saves progress and resumes from where you left off
• ⚡ Batching: Process multiple puzzles concurrently for faster evaluation
• 🎨 Rich Output: Beautiful terminal interface with progress tracking
• 🛑 Graceful Shutdown: Press Ctrl+C to stop after current batch

Example with different endpoints:

# OpenAI API
sparc --api-key "sk-..." --model "gpt-4"

# Custom endpoint (e.g., local model)
sparc --api-key "your-key" --base-url "http://localhost:8080/v1" --model "llama-3.3-70b"

# Resume interrupted session
sparc --api-key "your-key" --model "gpt-4"  # Automatically resumes

# Fresh start (ignore previous results)
sparc --api-key "your-key" --model "gpt-4" --overwrite

2. Using the Validation API

Use SPaRC's validation functions in your own code:

from sparc.validation import extract_solution_path, validate_solution, analyze_path
from sparc.prompt import generate_prompt
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("lkaesberg/SPaRC", "all", split="test")
puzzle = dataset[0]

# Generate prompt for your model
puzzle_prompt = [
                  {
                    "role": "system",
                    "content": "You are an expert at solving puzzles games.",
                  },
                  {
                    "role": "user", 
                    "content": generate_prompt(puzzle)
                  }
                ]


# Your model generates a response
model_response = "... model response with path coordinates ..."

# Extract the path from model response
extracted_path = extract_solution_path(model_response, puzzle)
# Returns: [{"x": 0, "y": 2}, {"x": 0, "y": 1}, ...]

# Validate against ground truth
is_correct = validate_solution(extracted_path, puzzle)
# Returns: True/False

# Get detailed analysis
analysis = analyze_path(extracted_path, puzzle)
# Returns: {
#   "starts_at_start_ends_at_exit": True,
#   "connected_line": True,
#   "non_intersecting_line": True,
#   "no_rule_crossing": True,
#   "fully_valid_path": True
# }

3. Running SPaRC with Inspect AI

SPaRC is also an Inspect AI task. Inspect is an optional extra, so install it with the package. From a checkout, run pip install -e ".[inspect]", then:

inspect eval sparc --model openai/gpt-4.1-mini            # all 500 test puzzles
inspect eval sparc_hard --model openai/gpt-4.1-mini       # the 89 hard (level 5) puzzles

With a normal (non-editable) install, for example pip install "sparc-puzzle[inspect]", the task names carry the package prefix: inspect eval sparc/sparc and inspect eval sparc/sparc_hard.

See sparc/README.md for options, scoring and reproduced numbers.

CLI Reference

Basic Usage

sparc --api-key "your-key" [OPTIONS]

Options

Examples

# Basic evaluation
sparc --api-key "sk-..." --model "gpt-4"

# High throughput with larger batches
sparc --api-key "sk-..." --model "gpt-3.5-turbo" --batch-size 20

# Conservative approach with lower temperature
sparc --api-key "sk-..." --model "gpt-4" --temperature 0.1

# Verbose output to see each puzzle result
sparc --api-key "sk-..." --model "gpt-4" --verbose

# Custom results file
sparc --api-key "sk-..." --model "claude-3" --results-file "claude_results.json"

# Process only 10 new puzzles
sparc --api-key "sk-..." --model "gpt-4" --max-new 10

# Step-by-step gym mode (agent receives feedback after each move)
sparc --api-key "sk-..." --model "gpt-4" --gym

# Gym mode with traceback (shows path history in observations)
sparc --api-key "sk-..." --model "gpt-4" --gym --gym-traceback

# Named experiment run
sparc --api-key "sk-..." --model "gpt-4" --run-name "experiment1"

Core Functions

extract_solution_path(solution_text: str, puzzle_data: Dict) -> List[Dict[str, int]]

Extracts coordinate path from model response text.

validate_solution(extracted_path: List[Dict[str, int]], puzzle_data: Dict) -> bool

Validates if the extracted path matches any ground truth solution.

analyze_path(solution_path: List[Dict[str, int]], puzzle: Dict) -> Dict

Provides detailed analysis of path validity and rule compliance.

generate_prompt(puzzle_data: Dict) -> str

Generates the formatted prompt for a puzzle.

Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues.

Citation

If you use SPaRC in your research, please cite:

@inproceedings{kaesberg-etal-2025-sparc,
  title     = "{SP}a{RC}: A Spatial Pathfinding Reasoning Challenge",
  author    = "Kaesberg, Lars Benedikt and Wahle, Jan Philip and Ruas, Terry and Gipp, Bela",
  booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
  month     = nov,
  year      = "2025",
  address   = "Suzhou, China",
  publisher = "Association for Computational Linguistics",
  url       = "https://aclanthology.org/2025.emnlp-main.526/",
  doi       = "10.18653/v1/2025.emnlp-main.526",
  pages     = "10359--10390"
}

Related work from our group

License

This project is licensed under the MIT License - see the LICENSE file for details.

Links

• 🌐 Website: sparc.gipplab.org
• 📚 Dataset: Hugging Face
• 🐛 Issues: GitHub Issues
• 📖 Documentation: GitHub Repository