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arxiv: 2511.21686 · v2 · submitted 2025-11-26 · 💻 cs.CL · cs.AI· cs.LG

Recognition: 2 theorem links

· Lean Theorem

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

Dong Wang , Yang Li , Ansong Ni , Ching-Feng Yeh , Youssef Emad , Xinjie Lei , Liam Robbins , Karthik Padthe
show 7 more authors
Hu Xu Xian Li Asli Celikyilmaz Ramya Raghavendra Lifei Huang Carole-Jean Wu Shang-Wen Li
Authors on Pith no claims yet

Pith reviewed 2026-05-17 04:17 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords synthetic data generationmulti-agent systemspeer-to-peer frameworkdecentralized orchestrationlanguage model trainingdata throughputdistributed computingagentic workflows
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The pith

Matrix uses peer-to-peer messaging to deliver 2-15 times higher throughput for multi-agent synthetic data generation without a central orchestrator.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Matrix as a decentralized framework for multi-agent synthetic data generation. It addresses bottlenecks in existing systems that rely on a central orchestrator by instead passing messages between agents through distributed queues. This allows independent task progression and better scaling to large numbers of workflows. The approach is evaluated on tasks including dialogue generation and tool-use trajectories, showing significant speed improvements without quality reduction. Such efficiency gains matter for producing diverse training data when real-world examples are hard to obtain.

Core claim

Matrix represents both control and data flow as serialized messages passed through distributed queues in a peer-to-peer design. This eliminates the central orchestrator, allowing each task to progress independently through lightweight agents while handling compute-intensive operations via distributed services. Built on Ray, it scales to tens of thousands of concurrent agentic workflows and is modular for adaptation to various data generation scenarios. Across evaluations in multi-agent collaborative dialogue, web-based reasoning data extraction, and tool-use trajectory generation, it achieves 2--15× higher data generation throughput under identical hardware resources without compromising the

What carries the argument

The peer-to-peer message-passing design through distributed queues that serializes control and data flow to enable decentralized coordination of multi-agent tasks.

If this is right

  • It scales to tens of thousands of concurrent agentic workflows on standard hardware.
  • It adapts modularly to diverse tasks such as collaborative dialogue and tool-use trajectory generation.
  • It maintains output quality while increasing generation speed by a factor of 2 to 15.
  • It offloads heavy computations like LLM inference to distributed services for efficient resource use.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Lower hardware needs could make large-scale synthetic dataset creation more accessible for smaller research groups.
  • Avoiding central control points may improve fault tolerance in data generation pipelines.
  • The approach might combine with other distributed platforms to support even broader workflow types.

Load-bearing premise

The peer-to-peer message-passing design and distributed services introduce no coordination overhead or reliability issues that would reduce effective throughput or output quality in production-scale deployments.

What would settle it

Observing throughput and output quality when running thousands of concurrent workflows in a production setting with network delays or agent failures.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces Matrix, a decentralized peer-to-peer framework for multi-agent synthetic data generation. Control and data flow are represented as serialized messages passed through distributed queues on Ray, eliminating the central orchestrator. Lightweight agents handle task progression while compute-intensive operations (LLM inference, containerized environments) are offloaded to distributed services. The system is designed to scale to tens of thousands of concurrent workflows and is evaluated on three scenarios: multi-agent collaborative dialogue, web-based reasoning data extraction, and tool-use trajectory generation in customer service settings. The central empirical claim is that Matrix delivers 2–15× higher data generation throughput than existing approaches under identical hardware resources while preserving output quality.

Significance. If the throughput claims are substantiated with complete baseline specifications, statistical controls, and overhead measurements, the work would offer a practical, modular system for scalable synthetic data production in LLM training pipelines. The peer-to-peer design and explicit separation of lightweight agents from heavy services address a recognized scalability limitation in centralized multi-agent frameworks. The modular, configurable architecture is a clear strength for cross-domain adaptation.

major comments (2)
  1. [Abstract and §5 (Evaluation)] Abstract and evaluation section: the reported 2–15× throughput gains are presented without naming the precise centralized baselines, their implementation details, hardware mapping, or whether equivalent distribution optimizations were applied to the comparators. This information is load-bearing for interpreting the magnitude of the improvement.
  2. [§3 (Architecture) and §4 (Implementation)] Architecture and implementation sections: the peer-to-peer message-passing design (serialized messages via Ray distributed queues) is described, but no isolated latency, serialization, or failure-recovery measurements are provided at the claimed scale of tens of thousands of concurrent workflows. Without these data, the assumption that coordination overhead remains negligible cannot be verified.
minor comments (2)
  1. [Abstract] Abstract: the specific quality metrics (e.g., human preference scores, automatic metrics, or diversity measures) used to confirm “no compromise in output quality” should be named explicitly.
  2. [Figures and Tables in §5] Figure and table captions: ensure all throughput plots and tables include error bars or standard deviations and state the number of independent runs.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the practical value of the peer-to-peer design for scalable synthetic data generation. We address each major comment below and indicate the revisions planned for the next version of the manuscript.

read point-by-point responses

  1. Referee: [Abstract and §5 (Evaluation)] Abstract and evaluation section: the reported 2–15× throughput gains are presented without naming the precise centralized baselines, their implementation details, hardware mapping, or whether equivalent distribution optimizations were applied to the comparators. This information is load-bearing for interpreting the magnitude of the improvement.

    Authors: We agree that greater specificity on the baselines is necessary for a fair interpretation of the throughput results. In the revised manuscript we will expand both the abstract and §5 to explicitly name the centralized comparator frameworks, detail their implementations (including Ray-based centralized orchestrators and other multi-agent baselines), specify the exact hardware allocations used for each, and confirm that no additional distribution optimizations were applied selectively to the baselines beyond standard practices. These clarifications will be added without altering the reported performance numbers. revision: yes

  2. Referee: [§3 (Architecture) and §4 (Implementation)] Architecture and implementation sections: the peer-to-peer message-passing design (serialized messages via Ray distributed queues) is described, but no isolated latency, serialization, or failure-recovery measurements are provided at the claimed scale of tens of thousands of concurrent workflows. Without these data, the assumption that coordination overhead remains negligible cannot be verified.

    Authors: We acknowledge that isolated micro-benchmarks would allow direct verification of the coordination-overhead assumption. While the end-to-end throughput results at scale already indicate that message-passing costs do not dominate, we will add a dedicated subsection in the revised version containing latency and serialization measurements for message queues at increasing concurrency levels (up to several thousand workflows) together with a description of the failure-recovery mechanisms already present in the Ray-based implementation. These additions will be presented as supplementary evidence rather than a change to the core claims. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical systems evaluation with direct throughput measurements

full rationale

The paper is a systems description of a decentralized multi-agent framework for synthetic data generation. Its central claims rest on empirical throughput comparisons (2-15x gains) measured under identical hardware across described tasks such as collaborative dialogue and tool-use trajectories. No mathematical derivations, fitted parameters, self-referential predictions, or load-bearing self-citations appear in the provided text; the results are presented as direct experimental outcomes rather than reductions to prior inputs or definitions. The evaluation is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard assumptions about reliable distributed messaging and the ability of lightweight agents to coordinate without central control; no free parameters or invented physical entities are introduced.

axioms (1)
  • domain assumption Distributed queues can handle serialized control and data messages for independent agent workflows with acceptable latency and reliability.
    Invoked in the description of the peer-to-peer design that eliminates the central orchestrator.

pith-pipeline@v0.9.0 · 5571 in / 1140 out tokens · 97768 ms · 2026-05-17T04:17:28.676261+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

16 extracted references · 16 canonical work pages · 7 internal anchors

  1. [1]

    https: //arxiv.org/abs/2412.08905. Yifan Bai, Yiping Bao, Guanduo Chen, Jiahao Chen, Ningxin Chen, Ruijue Chen, Yanru Chen, Yuankun Chen, Yutian Chen, Zhuofu Chen, Jialei Cui, Hao Ding, Mengnan Dong, Angang Du, Chenzhuang Du, Dikang Du, Yulun Du, Yu Fan, Yichen Feng, Kelin Fu, Bofei Gao, Hongcheng Gao, Peizhong Gao, Tong Gao, Xinran Gu, Longyu Guan, Haiqi...

    work page internal anchor Pith review Pith/arXiv arXiv

  2. [2]

    Kimi K2: Open Agentic Intelligence

    https://arxiv.org/abs/2507.20534. Amine Barrak, Mayssa Jaziri, Ranim Trabelsi, Fehmi Jaafar, and Fabio Petrillo. Spirt: A fault-tolerant and reliable peer-to-peer serverless ml training architecture, 2023.https://arxiv.org/abs/2309.14148. Victor Barres, Honghua Dong, Soham Ray, Xujie Si, and Karthik Narasimhan.τ 2-bench: Evaluating conversational agents i...

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  3. [3]

    https://arxiv.org/abs/2510.02387. CrewAI. Crewai: Open-source multi-agent framework for collaborative artificial intelligence.https://www.crewai.com,

    work page arXiv

  4. [4]

    Meta FAIR

    Accessed: 2025-10-22. Meta FAIR. Fastgen: Simple high-throughput inference library.https://github.com/facebookresearch/fastgen,

    work page 2025

  5. [5]

    Accessed: 2025-10-24. Adam Fourney, Gagan Bansal, Hussein Mozannar, Cheng Tan, Eduardo Salinas, Erkang, Zhu, Friederike Niedtner, Grace Proebsting, Griffin Bassman, Jack Gerrits, Jacob Alber, Peter Chang, Ricky Loynd, Robert West, Victor 14 Dibia, Ahmed Awadallah, Ece Kamar, Rafah Hosn, and Saleema Amershi. Magentic-one: A generalist multi-agent system fo...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  6. [6]

    SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

    Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. Swe-bench: Can language models resolve real-world github issues?, 2024.https://arxiv.org/abs/2310.06770. Gregory M. Kurtzer, Vanessa Sochat, and Michael W. Bauer. Singularity: Scientific containers for mobility of compute.PLoS ONE, 12(5):e0177459,

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  7. [7]

    https://doi.org/10.1371/journal

    doi: 10.1371/journal.pone.0177459. https://doi.org/10.1371/journal. pone.0177459. Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Syste...

    work page doi:10.1371/journal.pone.0177459

  8. [8]

    LangChain

    Accessed: 2025-10-24. LangChain. Langgraph: A low-level orchestration framework for stateful ai agents.https://langchain-ai.github.io/ langgraph/,

    work page 2025

  9. [9]

    Accessed: 2025-10-22. Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Gadre, Hritik Bansal, Etash Guha, Sedrick Keh, Kushal Arora, Saurabh Garg, Rui Xin, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee Chen, Suchin Gururangan, Mitchell Wortsman, Alon Albalak, Yonatan Bitton, Marianna Nezhurina, Amro Abbas, Cheng-Yu Hsieh, Dh...

    work page 2025

  10. [10]

    https://arxiv.org/abs/2406.11794. Arindam Mitra, Luciano Del Corro, Guoqing Zheng, Shweti Mahajan, Dany Rouhana, Andres Codas, Yadong Lu, Wei ge Chen, Olga Vrousgos, Corby Rosset, Fillipe Silva, Hamed Khanpour, Yash Lara, and Ahmed Awadallah. Agentinstruct: Toward generative teaching with agentic flows, 2024.https://arxiv.org/abs/2407.03502. Philipp Morit...

    work page arXiv 2024

  11. [11]

    gpt-oss-120b & gpt-oss-20b Model Card

    USENIX Association. ISBN 978-1-939133-08-3.https://www.usenix.org/conference/osdi18/ presentation/moritz. Ansong Ni, Ruta Desai, Yang Li, Xinjie Lei, Dong Wang, Jiemin Zhang, Jane Yu, Ramya Raghavendra, Gargi 16 Ghosh, Shang-Wen Li, and Asli Celikyilmaz. Collaborative reasoner: Self-improving social agents with synthetic conversations. InNeurIPS 2025, 202...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  12. [12]

    https://arxiv.org/abs/2504.14757. Akshara Prabhakar, Zuxin Liu, Ming Zhu, Jianguo Zhang, Tulika Awalgaonkar, Shiyu Wang, Zhiwei Liu, Haolin Chen, Thai Hoang, Juan Carlos Niebles, Shelby Heinecke, Weiran Yao, Huan Wang, Silvio Savarese, and Caiming Xiong. Apigen-mt: Agentic pipeline for multi-turn data generation via simulated agent-human interplay,

    work page arXiv

  13. [13]

    Zhen Qin, Xueqiang Yan, Mengchu Zhou, and Shuiguang Deng

    https://arxiv.org/abs/2504.03601. Zhen Qin, Xueqiang Yan, Mengchu Zhou, and Shuiguang Deng. Blockdfl: A blockchain-based fully decentralized peer-to-peer federated learning framework, 2024.https://arxiv.org/abs/2205.10568. Dingfeng Shi, Jingyi Cao, Qianben Chen, Weichen Sun, Weizhen Li, Hongxuan Lu, Fangchen Dong, Tianrui Qin, King Zhu, Minghao Liu, Jian ...

    work page arXiv 2024

  14. [14]

    MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

    https://arxiv.org/abs/2406.01574. Zhenting Wang, Qi Chang, Hemani Patel, Shashank Biju, Cheng-En Wu, Quan Liu, Aolin Ding, Alireza Rezazadeh, Ankit Shah, Yujia Bao, and Eugene Siow. Mcp-bench: Benchmarking tool-using llm agents with complex real-world tasks via mcp servers, 2025.https://arxiv.org/abs/2508.20453. Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yira...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  15. [15]

    Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauly, Michael J Franklin, Scott Shenker, and Ion Stoica

    https://arxiv.org/abs/2502.13124. Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauly, Michael J Franklin, Scott Shenker, and Ion Stoica. Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In9th USENIX Symposium on Networked Systems Design and Implementation (NSDI 12). USENIX A...

    work page arXiv

  16. [16]

    SGLang: Efficient Execution of Structured Language Model Programs

    Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Chuyue Sun, Jeff Huang, Cody Hao Yu, Shiyi Cao, Christos Kozyrakis, Ion Stoica, Joseph E. Gonzalez, Clark Barrett, and Ying Sheng. Sglang: Efficient execution of structured language model programs, 2024.https://arxiv.org/abs/2312.07104. 17

    work page internal anchor Pith review Pith/arXiv arXiv 2024