mlsanityData to 7 Oct 2026

Decentralized Multi-Agent System with Trust-Aware Communication

arXiv 2512.02410 ↗First posted 2 Dec 2025cs.MA cs.CRcited by 7

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

The emergence of Large Language Models (LLMs) is rapidly accelerating the development of autonomous multi-agent systems (MAS), paving the way for the Internet of Agents. However, traditional centralized MAS architectures present significant challenges, including single points of failure, vulnerability to censorship, inherent scalability limitations, and critical trust issues. We propose a novel Decentralized Multi-Agent System (DMAS) architecture designed to overcome these fundamental problems by enabling trust-aware, scalable, and censorship-resistant interactions among autonomous agents. Our DMAS features a decentralized agent runtime underpinned by a blockchain-based architecture. We formalize a trust-aware communication protocol that leverages cryptographic primitives and on-chain operations to provide security properties: verifiable interaction cycles, communication integrity, authenticity, non-repudiation, and conditional confidentiality, which we further substantiate through a comprehensive security analysis. Our performance analysis validates the DMAS as a scalable and efficient solution for building trustworthy multi-agent systems.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 16 of its 35 references that are in the map. First posted on arXiv 2 Dec 2025.

Builds on

16 of 35 references.

References · 16

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2023A Survey of Large Language Modelscited by 2,537
  4. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  5. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  6. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
  7. 2023A Survey on Evaluation of Large Language Modelscited by 1,227
  8. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 752
  9. 2022Large Language Models Can Self-Improvecited by 656
  10. 2023A Survey on Large Language Models for Recommendationcited by 378
  11. 2023Using an LLM to Help With Code Understandingcited by 222
  12. 2024Security and Privacy Challenges of Large Language Models: A Surveycited by 179
4 more
  1. 2024LLM Multi-Agent Systems: Challenges and Open Problemscited by 119
  2. 2024Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligencecited by 80
  3. 2022Self-Sovereign Identity as a Service: Architecture in Practicecited by 2
  4. 2023Leveraging Self-Sovereign Identity in Decentralized Data Aggregationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 6.

7 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Achilles Heel of Distributed Multi-Agent Systems
  2. 2025BlockA2A: Towards Secure and Verifiable Agent-to-Agent Interoperability
  3. 2025Towards Transparent and Incentive-Compatible Collaboration in Decentralized LLM Multi-Agent Systems: A Blockchain-Driven Approach
  4. 2025Enabling Regulatory Multi-Agent Collaboration: Architecture, Challenges, and Solutions
  5. 2025Byzantine-Robust Decentralized Coordination of LLM Agents
  6. 2025DAO-Agent: Zero Knowledge-Verified Incentives for Decentralized Multi-Agent Coordination
  7. 2025Seven Security Challenges in Cross-domain Multi-agent LLM Systems
  8. 2026Dynamic Trust-Aware Sparse Communication Topology for LLM-Based Multi-Agent Consensus