Holos: A Web-Scale LLM-Based Multi-Agent System for the Agentic Web
📰 ArXiv cs.AI
Holos is a web-scale LLM-based multi-agent system for the Agentic Web, addressing open-world issues in large language model-driven agents
Action Steps
- Design a scalable architecture for LLM-based multi-agent systems
- Develop coordination mechanisms to prevent breakdowns in agent interactions
- Implement value alignment methods to mitigate value dissipation
- Evaluate the system's performance in real-world scenarios
Who Needs to Know This
AI engineers and researchers benefit from Holos as it enables the development of autonomous digital entities that interact and co-evolve, while product managers and entrepreneurs can leverage Holos to create innovative applications for the Agentic Web
Key Insight
💡 Holos addresses open-world issues in LLM-based multi-agent systems, enabling the emergence of the Agentic Web and paving the way for Artificial General Intelligence (AGI)
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Key Takeaways
Holos is a web-scale LLM-based multi-agent system for the Agentic Web, addressing open-world issues in large language model-driven agents
Full Article
Title: Holos: A Web-Scale LLM-Based Multi-Agent System for the Agentic Web
Abstract:
arXiv:2604.02334v1 Announce Type: new Abstract: As large language models (LLM)-driven agents transition from isolated task solvers to persistent digital entities, the emergence of the Agentic Web, an ecosystem where heterogeneous agents autonomously interact and co-evolve, marks a pivotal shift toward Artificial General Intelligence (AGI). However, LLM-based multi-agent systems (LaMAS) are hindered by open-world issues such as scaling friction, coordination breakdown, and value dissipation. To a
Abstract:
arXiv:2604.02334v1 Announce Type: new Abstract: As large language models (LLM)-driven agents transition from isolated task solvers to persistent digital entities, the emergence of the Agentic Web, an ecosystem where heterogeneous agents autonomously interact and co-evolve, marks a pivotal shift toward Artificial General Intelligence (AGI). However, LLM-based multi-agent systems (LaMAS) are hindered by open-world issues such as scaling friction, coordination breakdown, and value dissipation. To a
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