Multi-Agent LLM Collaboration Using Confidence-Based Turn-Taking
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Solution Overview
Problem
Existing systems for organizational problem-solving, such as collaborative online whiteboards, fail to effectively integrate diverse perspectives and often require manual intervention, wasting processing resources and limiting the free flow of ideas.
Innovation Solution
A computerized method for collaboration among multiple large language model (LLM) agents that automatically selects agents based on confidence scores to generate ideas, allowing for dynamic and optimized resource usage, and enabling serendipitous development of ideas through turn-taking and non-linear collaboration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple LLM agents are used to provide diverse perspectives, then creativity and thorough exploration of ideas are enhanced, but device complexity and resource management become more challenging
Solution Approach 1:
The system segments the complex task of idea generation into multiple specialized LLM agents, each responsible for providing perspectives from different domains or methodologies. This segmentation allows diverse expertise to be distributed across independent agents rather than requiring one complex monolithic system.
Solution Approach 2:
A coordination mechanism acts as an intermediary between multiple LLM agents, managing their interactions, resolving conflicts, and synthesizing their outputs. This intermediary layer simplifies the complexity by providing a standardized interface for agents to communicate without requiring direct peer-to-peer coordination.
2Productivity
If manual intervention is required for idea generation and selection, then control over the process is maintained, but productivity and resource efficiency decrease
Solution Approach 1:
The LLM agents are designed to autonomously generate ideas, evaluate alternatives, and select the most promising solutions without requiring manual intervention. The system self-manages the ideation process by having agents independently perform tasks that would otherwise require human facilitation.
Solution Approach 2:
The system implements feedback loops where LLM agents evaluate each other's ideas and provide constructive criticism, allowing the system to self-correct and improve ideas iteratively without human input. This automated feedback mechanism maintains control while eliminating the need for manual review cycles.
3Loss of information
If diverse teams are assembled for brainstorming sessions, then comprehensive exploration of solutions is achieved, but loss of time for coordination and management increases
Solution Approach 1:
The system pre-configures multiple LLM agents with diverse knowledge bases and expertise areas before the brainstorming session begins. This preliminary preparation ensures that comprehensive perspectives are already available when needed, eliminating the time required to assemble and brief diverse human teams.
Solution Approach 2:
The LLM agents operate continuously without interruption, generating and evaluating ideas in real-time without the breaks, scheduling conflicts, or coordination delays inherent in human team sessions. The useful action of idea generation continues uninterrupted, maximizing productivity.
Data Source
AI summary
A multi-agent collaboration tool for collaborating among multiple large language model (LLM) agents is provided. A problem statement is received from a user. A first LLM agent is automatically selected to provide a first answer to the problem statement as a first confidence score for the first LLM agent is more than a second confidence score for a second LLM agent to provide the first answer to the problem statement. The second LLM agent is automatically selected to provide a second answer based on the first answer to the problem statement as a third confidence score for the second LLM agent is more than a fourth confidence score for the first LLM agent to provide the second answer based on the first answer to the problem statement. A solution to the problem statement is provided based on the second answer.