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license: unlicense
task_categories:
- question-answering
language:
- en
tags:
- agent
pretty_name: Million Agent Sorting Challenge
size_categories:
- 1M<n<10M
---
# Million Agent Sorting Challenge (MASC)
Benchmarking Large Scale Multi-Agent Systems Through Coordinated Sorting
---
## Overview
The **Million Agent Sorting Challenge (MASC)** is a benchmark designed to study **scalability, coordination, and communication** in large-scale multi-agent systems (MAS).
Each agent receives a shuffled subset of integers and must **cooperate** with others through communication channels (e.g., shared blackboard, messaging, or tool calls) to collectively produce a globally sorted list.
MASC evaluates how efficiently an MAS can perform **distributed computation**, **state synchronization**, and **coordinated decision-making** as the number of agents grows from **10¹ to 10⁶**.
---
## Task Definition
Each instance defines:
* **n**: number of agents
* **k**: number of integers per agent
* **xs[i]**: input list for agent *i*
* **ys[i]**: output list expected from agent *i*
Agents collectively must satisfy:
```python
def check_results(n, k, xs, ys):
xl = sorted(sum(xs, []))
yl = sum(ys, [])
return xl == sorted(yl)
```
---
## Dataset Structure
Each JSON file (`task_n{n}_k{k}.json`) contains:
```json
{
"n": 1000000,
"k": 10,
"agents": [
{
"agent_id": 0,
"prompt": "... task description ...",
"input_data": [...],
"expected_output": [...]
},
...
]
}
```
Data sizes scale from `n = 10¹` up to `n = 10⁶`, allowing controlled experiments on MAS scalability and emergent coordination.
---
## Intended Use
This dataset can be used for:
* Studying **scaling laws of cooperation**
* Designing **communication protocols** and **distributed reasoning architectures**
* Benchmarking **multi-agent emergent behavior** at increasing system sizes
---
## Citation
> *Preprint coming soon.* |