Advancements in Multi-Agent Systems and Large Language Models

The field of multi-agent systems and large language models (LLMs) is rapidly evolving, with a clear trend towards enhancing collaboration, adaptability, and efficiency in complex environments. Recent developments focus on overcoming communication delays, improving self-improvement mechanisms through multiagent finetuning, and exploring the collaborative potential of LLM-based multi-agent systems (MASs). Innovations include frameworks for handling asynchronous communication, strategies for diversifying reasoning chains, and architectures that support interoperability and reconfigurability in system of systems (SoS). Additionally, there's a push towards democratizing LLMs through blockchain-based networks and enhancing the cognitive depth of models via self-rethinking mechanisms. These advancements aim to address real-world challenges, such as computational resource constraints, the need for up-to-date expert knowledge, and the integration of multimodal data for decision-making.

Noteworthy papers include:

  • CoDe: Introduces a novel framework for communication delay-tolerant multi-agent collaboration, significantly improving performance under fixed and time-varying delays.
  • Multiagent Finetuning: Proposes a multiagent approach to LLM self-improvement, enabling specialization and diversification across models.
  • LLM-Net: A blockchain-based framework that democratizes LLMs-as-a-Service, ensuring sustained knowledge growth and service quality.
  • GRAPHMOE: Enhances the cognitive depth of Mixture-of-Experts networks through a self-rethinking mechanism, achieving state-of-the-art performance.
  • LLM-Ehnanced Holonic Architecture: Advances holonic architecture for SoS, improving interoperability and reconfigurability with LLM-enhanced decision-making.

Sources

CoDe: Communication Delay-Tolerant Multi-Agent Collaboration via Dual Alignment of Intent and Timeliness

Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains

Multi-Agent Collaboration Mechanisms: A Survey of LLMs

The Internet of Large Language Models: An Orchestration Framework for LLM Training and Knowledge Exchange Toward Artificial General Intelligence

LLM-Net: Democratizing LLMs-as-a-Service through Blockchain-based Expert Networks

Agent-Centric Projection of Prompting Techniques and Implications for Synthetic Training Data for Large Language Models

GRAPHMOE: Amplifying Cognitive Depth of Mixture-of-Experts Network via Introducing Self-Rethinking Mechanism

LLM-Ehnanced Holonic Architecture for Ad-Hoc Scalable SoS

Drama Llama: An LLM-Powered Storylets Framework for Authorable Responsiveness in Interactive Narrative

LLM-Based Routing in Mixture of Experts: A Novel Framework for Trading

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