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Agent Prune: A Robust and Economic Multi-Agent Communication Framework for LLMs that Saves Cost and Removes Redundant and Malicious Contents

Agent Prune: A Robust and Economic Multi-Agent Communication Framework for LLMs that Saves Cost and Removes Redundant and Malicious Contents

Collaboration for Better Results

“If you want to go fast, go alone. If you want to go far, go together.” This African proverb highlights how multi-agent systems can outperform individual LLMs in reasoning and creativity tasks. By leveraging the combined intelligence of multiple LLMs through effective communication, these systems achieve impressive results. However, this comes with increased token usage, which can cost significantly more—up to 12 times the usual consumption.

Introducing AgentPrune

Researchers from Tongji University and Shanghai AI Laboratory identified Communication Redundancy in multi-agent systems. They discovered that many messages exchanged between agents do not contribute to the outcome. To address this, they developed AgentPrune, a framework that optimizes communication by eliminating redundancy.

How AgentPrune Works

AgentPrune treats multi-agent communication as a graph and uses a low-rank principle to prune unnecessary messages. It employs two methods:

  • Spatial Pruning: Removes redundant messages during a conversation.
  • Temporal Pruning: Eliminates irrelevant past dialogue.

Types of Communication

AgentPrune focuses on two communication strategies:

  • Intra-dialogue Communication: Collaboration within a single session.
  • Inter-dialogue Communication: Information carried over multiple sessions.

In the AgentPrune model, agents are represented as nodes, and their interactions as edges, allowing for efficient communication.

Easy Integration and Testing

This algorithm is straightforward to integrate into existing multi-agent systems, optimizing token usage effectively. It works best with more than three agents and structured communication. AgentPrune also utilizes Multi-Query Training to minimize the number of queries needed.

Proven Results

AgentPrune was tested on various tasks, including General Reasoning and Code Generation, using five GPT-4 models. Key findings include:

  • Not all multi-agent setups provided better performance.
  • High-quality results were achieved while reducing costs.

Additionally, AgentPrune enhances security by filtering out harmful messages, ensuring robust performance even under attack.

Maximizing the Benefits of AI

AgentPrune streamlines multi-agent communication, ensuring accuracy while saving costs. It embodies a practical approach to achieving results without unnecessary expenses.

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