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:
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:
{
"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.