mlsanityData to 14 Oct 2026

DAWN: Designing Distributed Agents in a Worldwide Network

arXiv 2410.22339 ↗First posted 11 Oct 2024cs.NI cs.AI cs.MAcited by 6

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

Abstract (arXiv)

The rapid evolution of Large Language Models (LLMs) has transformed them from basic conversational tools into sophisticated entities capable of complex reasoning and decision-making. These advancements have led to the development of specialized LLM-based agents designed for diverse tasks such as coding and web browsing. As these agents become more capable, the need for a robust framework that facilitates global communication and collaboration among them towards advanced objectives has become increasingly critical. Distributed Agents in a Worldwide Network (DAWN) addresses this need by offering a versatile framework that integrates LLM-based agents with traditional software systems, enabling the creation of agentic applications suited for a wide range of use cases. DAWN enables distributed agents worldwide to register and be easily discovered through Gateway Agents. Collaborations among these agents are coordinated by a Principal Agent equipped with reasoning strategies. DAWN offers three operational modes: No-LLM Mode for deterministic tasks, Copilot for augmented decision-making, and LLM Agent for autonomous operations. Additionally, DAWN ensures the safety and security of agent collaborations globally through a dedicated safety, security, and compliance layer, protecting the network against attackers and adhering to stringent security and compliance standards. These features make DAWN a robust network for deploying agent-based applications across various industries.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 53 of its 88 references that are in the map. First posted on arXiv 11 Oct 2024.

Builds on

53 of 88 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies28 Jul
  2. 2024Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities10 Jul
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  5. 2023"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents20 Sep

Other references · 48

  1. 2024The Llama 3 Herd of Modelscited by 14,715
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  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
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  6. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
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36 more
  1. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 1.

6 papers.

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