IMO 2025 Problem Solver

A parallel AI agent system for solving International Mathematical Olympiad (IMO) problems using Google's Gemini API.

MIT License

Copyright (c) 2025 Lin Yang, Yichen Huang

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Overview

This project consists of the following components:

• code/agent.py: A single AI agent that attempts to solve IMO problems
• code/run_parallel.py: A parallel execution system that runs multiple agents simultaneously
• code/res2md.py: A small utility to parse a result file that contains JSON (e.g., JSONL) and print the last JSON object

Prerequisites

1. Python 3.7+ installed on your system
2. Google Gemini API key - Get one from Google AI Studio
3. Required Python packages:

   pip install requests

Setup

1. Clone or download the project files
2. Set up your API key:
   • Create a .env file in the project directory
   • Add your API key: GOOGLE_API_KEY=your_api_key_here
   • Or set it as an environment variable: export GOOGLE_API_KEY=your_api_key_here

Usage

Single Agent (agent.py)

Run a single agent to solve an IMO problem:

python agent.py problem.txt [options]

Arguments:

• problem.txt: Path to the problem statement file (required); imo2025 problems are in problems

Options:

• --log LOG_FILE: Specify a log file for output (default: prints to console)
• --other_prompts PROMPTS: Additional prompts separated by commas

Example:

python agent.py imo2025_p1.txt --log agent_output.log

Parallel Execution (code/run_parallel.py)

Run multiple agents in parallel to increase the chance of finding a solution:

python IMO25/code/run_parallel.py <problem_file> [options]

Arguments:

• problem.txt: Path to the problem statement file (required). Use an absolute path or ensure the path is valid from within IMO25/code/ (the script runs the agent with its working directory set to IMO25/code/).

Options:

• --num-agents N or -n N: Number of parallel agents (default: 10)
• --log-dir DIR or -d DIR: Directory for log files (default: logs)
• --timeout SECONDS or -t SECONDS: Timeout per agent in seconds (default: no timeout)
• --max-workers N or -w N: Maximum worker processes (default: number of agents)
• --other_prompts PROMPTS or -o PROMPTS: Additional prompts separated by commas
• --agent-file PATH or -a PATH: Path to the agent file to run (default: agent.py inside IMO25/code/)
• --exit-immediately or -e: Exit the whole run as soon as any agent finds a correct solution (otherwise, all agents run to completion)

Examples:

# Run 20 agents with 5-minute timeout each
python IMO25/code/run_parallel.py problems/imo2025_p1.txt -n 20 -t 300

# Run 5 agents with custom log directory and exit immediately on first success
python IMO25/code/run_parallel.py problems/imo2025_p1.txt -n 5 -d logs/p1_run -e

# Run with additional prompts and a custom agent file
python IMO25/code/run_parallel.py problems/imo2025_p1.txt -n 15 -o "focus_on_geometry,use_induction" -a agent.py

Result extractor (code/res2md.py)

Parse a result file that contains JSON (for example, a .jsonl file where each line is a JSON object), and print the last JSON object in the file. Useful for quickly extracting the final structured result produced by some runs.

python IMO25/code/res2md.py <result_file>

Example:

python IMO25/code/res2md.py logs/results.jsonl

Problem File Format

See the problems folder.

Output and Logging

Single Agent

• Output is printed to console by default
• Use --log to save output to a file
• The agent will indicate if a complete solution was found

Parallel Execution

• Each agent creates a separate log file in the specified directory
• Progress is shown in real-time
• Final summary shows:
  • Total execution time
  • Number of successful/failed agents
  • Success rate
  • Which agent found a solution (if any)
  • Location of log files

Understanding the Output

Solution Detection

The system looks for the phrase "Found a correct solution in run" to identify successful solutions.

Agent Behavior

• Agents use Google's Gemini 2.5 Pro model
• Each agent follows a structured approach with multiple attempts
• Solutions are verified for completeness and correctness
• Agents can provide partial solutions if complete solutions aren't found

Tips for Best Results

1. Problem Formatting: Ensure your problem file is clear and well-formatted
2. Parallel Execution: Use more agents for harder problems (10-20 agents recommended)
3. Timeout Settings: Set reasonable timeouts (you may set no timeout)
4. API Limits: Be aware of Google API rate limits and costs
5. Log Analysis: Check individual agent logs for detailed reasoning

Troubleshooting

Common Issues

1. API Key Error: Ensure your Google API key is properly set
2. Timeout Issues: Increase timeout or reduce number of agents
3. Memory Issues: Reduce max-workers if running out of memory
4. No Solutions Found: Try running more agents or check problem clarity

Debug Mode

Add verbose logging by modifying the agent code or check individual log files for detailed output.

License

MIT License - Copyright (c) 2025 Lin Yang, Yichen Huang

This software is provided as-is. Users are free to copy, modify, and distribute the code with proper attribution.

Contributing

Feel free to submit issues, feature requests, or pull requests to improve the system.

Community Codes

Community contributions are located in code/community_codes/. These have not been thoroughly tested, so please use them at your own risk.

Disclaimer

This tool is for educational and research purposes. Success in solving IMO problems depends on problem difficulty and AI model capabilities. Not all problems may be solvable by the current system.

Citation

If you use this code in your research, please cite:

@article{huang2025gemini,
  title={Gemini 2.5 Pro Capable of Winning Gold at IMO 2025},
  author={Huang, Yichen and Yang, Lin F},
  journal={arXiv preprint arXiv:2507.15855},
  year={2025}
}