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

arXiv:2511.21686v2 Announce Type: replace-cross 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, cr