Matrix: Peer-to-Peer Multi-Agent Synthetic Data Generation Framework

• URL: http://arxiv.org/abs/2511.21686v1
• Date: Wed, 26 Nov 2025 18:59:28 GMT
• Title: Matrix: Peer-to-Peer Multi-Agent Synthetic Data Generation Framework
• Authors: Dong Wang, Yang Li, Ansong Ni, Ching-Feng Yeh, Youssef Emad, Xinjie Lei, Liam Robbins, Karthik Padthe, Hu Xu, Xian Li, Asli Celikyilmaz, Ramya Raghavendra, Lifei Huang, Carole-Jean Wu, Shang-Wen Li,
• Abstract summary: We present textbf Matrix, a decentralized framework for multi-agent synthesis.<n> Matrix represents both control and data flow as serialized messages pass through distributed queues.<n>We evaluate Matrix across diverse synthesis scenarios, such as multi-agent collaborative dialogue, web-based reasoning data extraction, and tool-use trajectory generation in customer service environments.
• Score: 32.3041485160475
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Synthetic data has become increasingly important for training large language models, especially when real data is scarce, expensive, or privacy-sensitive. Many such generation tasks require coordinated multi-agent workflows, where specialized agents collaborate to produce data that is higher quality, more diverse, and structurally richer. However, existing frameworks for multi-agent synthesis often depend on a centralized orchestrator, creating scalability bottlenecks, or are hardcoded for specific domains, limiting flexibility. We present \textbf{Matrix}, a decentralized framework that represents both control and data flow as serialized messages passed through distributed queues. This peer-to-peer design eliminates the central orchestrator. Each task progresses independently through lightweight agents, while compute-intensive operations, such as LLM inference or containerized environments, are handled by distributed services. Built on Ray, Matrix scales to tens of thousands of concurrent agentic workflows and provides a modular, configurable design that enables easy adaptation to a wide range of data generation workflows. We evaluate Matrix across diverse synthesis scenarios, such as multi-agent collaborative dialogue, web-based reasoning data extraction, and tool-use trajectory generation in customer service environments. In all cases, Matrix achieves $2$--$15\times$ higher data generation throughput under identical hardware resources, without compromising output quality.

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