Enhance dataset card: Add paper, project page, GitHub, metadata

README.md

@@ -1,4 +1,11 @@
 ---
+task_categories:
+- graph-ml
+license: mit
+tags:
+- multi-agent-systems
+- benchmark
+- llms
 dataset_info:
   features:
   - name: 'Unnamed: 0'
@@ -23,3 +30,33 @@ configs:
   - split: train
     path: data/train-*
 ---
+
+# AgentsNet
+
+This repository contains the graph instances used in the [AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs](https://huggingface.co/papers/2507.08616) paper.
+
+AgentsNet is a new benchmark for multi-agent reasoning, designed to measure the ability of multi-agent systems to collaboratively form strategies for problem-solving, self-organization, and effective communication given a network topology. It draws inspiration from classical problems in distributed systems and graph theory.
+
+*   **Paper**: [AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs](https://huggingface.co/papers/2507.08616)
+*   **Project Page**: [https://agentsnet.graphben.ch](https://agentsnet.graphben.ch)
+*   **GitHub Repository**: [https://github.com/ashleve/lightning-hydra-template](https://github.com/ashleve/lightning-hydra-template)
+
+## Dataset
+
+The dataset consists of synthetic graphs generated using various random graph models. It serves as the input for all experiments in the benchmark.
+
+## Citation
+
+If you use this dataset, please cite the associated paper:
+
+```bibtex
+@misc{grötschla2025agentsnetcoordinationcollaborativereasoning,
+      title={AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs}, 
+      author={Florian Grötschla and Luis Müller and Jan Tönshoff and Mikhail Galkin and Bryan Perozzi},
+      year={2025},
+      eprint={2507.08616},
+      archivePrefix={arXiv},
+      primaryClass={cs.MA},
+      url={https://arxiv.org/abs/2507.08616}, 
+}
+```