README.md

---
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.*