Million Agent Sorting Challenge (MASC)

Benchmarking Large Scale Multi-Agent Systems Through Coordinated Sorting

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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.

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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:

def check_results(n, k, xs, ys):
    xl = sorted(sum(xs, []))
    yl = sum(ys, [])
    return xl == sorted(yl)

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Dataset Structure

Each JSON file (task_n{n}_k{k}.json) contains:

{
  "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.

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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

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Citation

Preprint coming soon.