Datasets:
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---
task_categories:
- graph-ml
license: mit
tags:
- multi-agent-systems
- benchmark
- llms
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: graph_generator
dtype: string
- name: num_nodes
dtype: int64
- name: index
dtype: int64
- name: graph
dtype: string
splits:
- name: train
num_bytes: 517975
num_examples: 117
download_size: 127270
dataset_size: 517975
configs:
- config_name: default
data_files:
- 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},
}
``` |