Multiagent Debate via Embedding Communication Protocol
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Solution Overview
Problem
Current multiagent debate systems relying on natural language communication face inefficiencies due to information loss during token sampling, which limits the effectiveness of less powerful language models in benefiting from feedback and debate processes.
Innovation Solution
Implementing a communication protocol called CIPHER (Communicative Inter-Model Protocol Through Embedding Representation) that allows agents to communicate through embedding representations instead of natural language, bypassing token sampling and enabling richer information transfer by using expectation of raw transformer output embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If natural language communication is used for multiagent debate, then the system is easier to understand and implement, but information loss during token sampling reduces the effectiveness of feedback and debate processes
Solution Approach 1:
The patent introduces embedding representations as an intermediary medium between agents, replacing direct natural language communication. These embeddings serve as a lossless communication channel that preserves full information from transformer outputs, eliminating token sampling artifacts while maintaining system functionality.
Solution Approach 2:
The patent changes the communication parameter from discrete natural language tokens to continuous embedding vectors. This parameter transformation allows agents to exchange rich, nuanced information without the information loss inherent in discrete tokenization, enabling more effective debate and feedback processes.
2Productivity
If embedding representations are used for communication, then information transfer efficiency is improved, but the system complexity increases
Solution Approach 1:
The patent leverages the self-contained nature of embedding representations, which inherently encode all necessary information from the transformer model's output. Each agent can independently generate and interpret embeddings without requiring additional coordination or translation mechanisms, simplifying the overall system architecture despite the advanced communication method.
Data Source
AI summary
There are provided solutions for multiagent debate. In a method, a first and a second response representation are determined by a first and a second agent in a plurality of agents based on a first query representation for a query in a natural language, respectively, and the first and second response representations are convertible to a first and a second answer to the query in the natural language, respectively. A second query representation is obtained based on the first query representation, and at least one of the first and second response representations. A response representation is determined based on the second query representation by at least one of the first and second agents, and the response representation is convertible to an answer to the query in the natural language. These agents communicate in an embedding space without a conversion between a natural language format and an embedding format.