Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems

2025-05-18

AI Summary

This work addresses the challenge in multi-agent systems where coarse-grained, opaque collaboration strategies hinder simultaneous optimization of performance and efficiency. We first systematically decompose collaboration into four fine-grained dimensions: agent governance, participation control, interaction dynamics, and dialogue history management. To jointly quantify accuracy and computational cost, we propose the Token-Accuracy Ratio (TAR) metric. Methodologically, we design a context-aware policy controller and a dynamic history compression–abstraction generation technique. Experiments on DEI and SES benchmarks show that the optimal policy combination improves accuracy by 19.3% while reducing token consumption by 32.7%. Our analysis reveals synergistic optimization principles: centralized governance, mentor-led participation, ordered interaction, and teacher-refined summarization. This work shifts the multi-agent design paradigm from architectural innovation toward principled, mechanism-level policy innovation.

Technology Category

• Multiagent Systems: Mechanism Design
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Multi-agent collaboration has emerged as a pivotal paradigm for addressing complex, distributed tasks in large language model (LLM)-driven applications. While prior research has focused on high-level architectural frameworks, the granular mechanisms governing agents, critical to performance and scalability, remain underexplored. This study systematically investigates four dimensions of collaboration strategies: (1) agent governance, (2) participation control, (3) interaction dynamics, and (4) dialogue history management. Through rigorous experimentation under two context-dependent scenarios: Distributed Evidence Integration (DEI) and Structured Evidence Synthesis (SES), we quantify the impact of these strategies on both task accuracy and computational efficiency. Our findings reveal that centralized governance, instructor-led participation, ordered interaction patterns, and instructor-curated context summarization collectively optimize the trade-off between decision quality and resource utilization with the support of the proposed Token-Accuracy Ratio (TAR). This work establishes a foundation for designing adaptive, scalable multi-agent systems, shifting the focus from structural novelty to strategic interaction mechanics.

Problem

• Investigates granular collaboration strategies in multi-agent systems
• Quantifies impact of agent governance and interaction dynamics
• Optimizes trade-off between decision quality and resource utilization

Innovation

• Investigates agent governance and participation control
• Analyzes interaction dynamics and dialogue management
• Proposes Token-Accuracy Ratio for optimization