Dynamic Role Assignment for Multi-Agent Debate

2026-01-23

AI Summary

Existing multi-agent debate systems typically employ static or random role assignment, overlooking individual model expertise and thereby limiting overall performance. This work proposes a dynamic role allocation framework that leverages a meta-debate mechanism to match the most suitable agents to specific roles prior to the formal debate, comprising proposal and peer-review phases. The framework introduces a capability-aware role assignment paradigm, integrating large language models (LLMs) with vision-language models (VLMs) to enable role-customized argument generation and a scoring mechanism grounded in both data and role-specific criteria. Experimental results demonstrate that, across multiple LLM-based problem-solving benchmarks, the proposed approach achieves up to a 74.8% improvement over uniform model assignment and up to a 29.7% gain compared to random allocation.

Technology Category

• Multiagent Systems: Agreement, Argumentation & Negotiation
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Multi-agent large language model (LLM) and vision-language model (VLM) debate systems employ specialized roles for complex problem-solving, yet model specializations are not leveraged to decide which model should fill which role. We propose dynamic role assignment, a framework that runs a Meta-Debate to select suitable agents before the actual debate. The meta-debate has two stages: (1) proposal, where candidates provide role-tailored arguments, and (2) peer review, where proposals are scored with data and role-specific criteria to choose the best agent for each position. We evaluate our method on LLM problem solving benchmarks. Applied on top of existing debate systems, our approach consistently outperforms uniform assignments (filling all roles with the same model) by up to 74.8% and random assignments (assigning models to roles without considering their suitability) by up to 29.7%, depending on the task and the specific assignment. This work establishes a new paradigm for multi-agent system design, shifting from static agent deployment to dynamic and capability-aware selection.

Problem

• multi-agent debate
• role assignment
• large language models
• model specialization
• dynamic selection

Innovation

• Dynamic Role Assignment
• Meta-Debate
• Multi-Agent Debate
• Capability-Aware Selection
• Large Language Models