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README.md

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-# FrontierCO
 
-A Comprehensive Benchmark for Evaluating Contemporary ML-Based Solvers on Challenge Combinatorial Optimization Problems
+# FrontierCO: Benchmark Dataset for Frontier Combinatorial Optimization
+
+## Overview
+
+**FrontierCO** is a curated benchmark suite for evaluating ML-based solvers on large-scale and real-world **Combinatorial Optimization (CO)** problems. The benchmark spans **8 classical CO problems** across **5 application domains**, providing both training and evaluation instances specifically designed to test the frontier of ML and LLM capabilities in solving NP-hard problems.
+
+Combinatorial optimization plays a fundamental role in discrete mathematics, computer science, and operations research, with applications in routing, scheduling, allocation, and more. As ML-based solvers evolve—ranging from neural networks to symbolic reasoning with large language models—**FrontierCO** offers the first comprehensive dataset suite tailored to test these solvers at realistic scales and difficulties.
+
+---
+
+## Dataset Structure
+
+Each subdirectory corresponds to a specific CO task:
+
+```
+FrontierCO/
+├── CFLP/
+│   ├── easy_test_instances/
+│   ├── hard_test_instances/
+│   ├── valid_instances/
+│   └── config.py
+├── CPMP/
+├── CVRP/
+├── FJSP/
+├── MIS/
+├── MDS/
+├── STP/
+├── TSP/
+└── ...
+```
+
+Each task folder contains:
+
+* `easy_test_instances/`: Benchmark instances that are solvable by SOTA human-designed solvers.
+* `hard_test_instances/`: Instances that remain computationally intensive or lack known optimal solutions.
+* `valid_instances/` *(if applicable)*: Additional instances for validation or development.
+* `config.py`: Metadata about instance format, solver settings, and reference solutions.
+
+---
+
+## Tasks Covered
+
+The benchmark currently includes the following problems:
+
+* **MIS** – Maximum Independent Set
+* **MDS** – Minimum Dominating Set
+* **TSP** – Traveling Salesman Problem
+* **CVRP** – Capacitated Vehicle Routing Problem
+* **CFLP** – Capacitated Facility Location Problem
+* **CPMP** – Capacitated p-Median Problem
+* **FJSP** – Flexible Job-shop Scheduling Problem
+* **STP** – Steiner Tree Problem
+
+Each task includes:
+
+* Easy and hard test sets with varying difficulty and practical relevance
+* Training and validation instances where applicable, generated using problem-specific generators
+* Reference results for classical and ML-based solvers
+
+---
+
+## Data Sources
+
+Instances are sourced from a mix of:
+
+* Public repositories (e.g., [TSPLib](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/), [CVRPLib](http://vrp.galgos.inf.puc-rio.br/))
+* DIMACS and PACE Challenges
+* Synthetic instance generators used in prior ML and optimization research
+* Manual curation from recent SOTA solver evaluation benchmarks
+
+For tasks lacking open benchmarks, we include high-quality synthetic instances aligned with real-world difficulty distributions.
+
+---
+
+## Usage
+
+To use this dataset, clone the repository and select the task of interest. Each `config.py` file documents the format and how to parse or evaluate the instances.
+
+```bash
+git clone https://huggingface.co/datasets/<your-username>/FrontierCO
+cd FrontierCO/CFLP
+python config.py  # example: parse instances or evaluate solver
+```
+
+---
+
+## Citation
+
+If you use **FrontierCO** in your research or applications, please cite the following paper:
+
+```bibtex
+@misc{feng2025comprehensive,
+  title={A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial Optimization},
+  author={Shengyu Feng and Weiwei Sun and Shanda Li and Ameet Talwalkar and Yiming Yang},
+  year={2025},
+  eprint={xxxx.xxxxx},
+  archivePrefix={arXiv},
+  primaryClass={cs.AI}
+}
+```
+
+---
+
+## License
+
+This dataset is released under the MIT License. Refer to `LICENSE` file for details.
+
+---