Matrix: Peer-to-Peer Synthetic Data at Scale

Kari Jaaskelainen

27 Nov 2025 — 1 min read

Matrix: Faster, flexible synthetic data—without a central bottleneck

Training AI often needs synthetic data, especially when real data is scarce, pricey, or private. But most generators rely on a central “traffic cop,” slowing things down.

Matrix flips the script with a peer‑to‑peer design. Tiny specialized agents pass messages directly through distributed queues, so tasks move independently. Heavy lifting (like LLM calls or sandboxed tools) runs on scalable services.

Throughput: 2–15× more data on the same hardware
Scale: tens of thousands of concurrent workflows
Flexible: modular, configurable, and domain‑agnostic

Tested across collaborative dialogues, web‑based reasoning extraction, and customer‑service tool‑use traces, Matrix boosted speed without hurting quality.

Think of it as replacing a busy call center with a smart peer network—faster, cheaper, and easier to adapt.

Paper: Matrix: Peer-to-Peer Multi-Agent Synthetic Data Generation Framework — https://arxiv.org/abs/2511.21686v1

Paper: https://arxiv.org/abs/2511.21686v1

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#AI #SyntheticData #LLM #MultiAgent #DistributedSystems #MLOps #NLP #Scalability

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Matrix: Faster, flexible synthetic data—without a central bottleneck

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Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A