Add dataset card and metadata for RandSATBench

Hello! I'm Niels, part of the community science team at Hugging Face. I noticed that this dataset repository doesn't have a dataset card yet. I've opened this PR to add a README that includes:
- Metadata for task categories (`graph-ml`) and license (`mit`).
- Links to the associated paper and GitHub repository.
- Dataset statistics for the K-SAT and q-coloring benchmarks.
- Instructions on how to download or generate the datasets as described in the official repository.

This will help researchers better discover and utilize your benchmark on the Hugging Face Hub!

README.md

@@ -0,0 +1,60 @@
+---
+license: mit
+task_categories:
+- graph-ml
+---
+
+# RandSATBench: Benchmarks for Constraint Satisfaction Problems
+
+This repository contains the benchmarks presented in the paper [Benchmarking Graph Neural Networks in Solving Hard Constraint Satisfaction Problems](https://huggingface.co/papers/2602.18419).
+
+**RandSATBench** provides a mixed set of easy, hard, and unsatisfiable instances of constraint satisfaction problems, specifically *q-coloring* and *Boolean satisfiability* (K-SAT) problems. The goal is to provide a challenging setting to compare the performance of deep learning methods (particularly Graph Neural Networks) against classical exact and heuristic solvers.
+
+- **Paper:** [https://huggingface.co/papers/2602.18419](https://huggingface.co/papers/2602.18419)
+- **Repository:** [https://github.com/ArtLabBocconi/RandCSPBench](https://github.com/ArtLabBocconi/RandCSPBench)
+
+## Datasets
+
+The benchmark includes datasets for Boolean satisfiability (3-SAT and 4-SAT) and coloring problems (3-coloring and 5-coloring). The instances vary in size ($N=16, 32, 64, 256$) and connectivity.
+
+| Dataset | # Train Instances | # Test Instances |
+|---------|-------------------|------------------|
+| 3-SAT   | 168,000           | 42,000           | 
+| 4-SAT   | 84,000            | 21,000           | 
+| 3-col   | 60,000            | 20,000           | 
+| 5-col   | 60,000            | 20,000           | 
+
+## Usage
+
+### K-SAT
+
+The random instances of 3-SAT or 5-SAT problems in CNF format can be downloaded using the following:
+
+```bash
+wget https://huggingface.co/datasets/CarloLucibello/kSAT-Benchmarks/resolve/main/kSAT.zip
+unzip kSAT.zip
+```
+
+Ground truth solutions obtained by running the CaDiCal solver are available in the corresponding `train_labels.csv` files.
+
+### q-coloring
+
+To generate the random graphs for the 3-coloring and 5-coloring benchmarks, use the scripts provided in the GitHub repository:
+
+```bash
+cd datasets
+python gen_graphs_coloring.py
+```
+
+## Citation
+
+If you use this benchmark in your research, please cite:
+
+```bibtex
+@article{lucibello2026benchmarking,
+  title={Benchmarking Graph Neural Networks in Solving Hard Constraint Satisfaction Problems},
+  author={Lucibello, Carlo and others},
+  journal={arXiv preprint arXiv:2602.18419},
+  year={2026}
+}
+```