AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction

ACL ARR 2025 May Submission7291 Authors

Abstract:

Recent progress in large language model (LLM)-based multi-agent collaboration highlights the power of structured communication in enabling collective intelligence. However, existing methods largely rely on static or graph-based inter-agent topologies, lacking the potential adaptability and flexibility in communication.
In this work, we propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure, offering a significantly larger topology space for multi-agent communication. Our method focuses on two key directions: (1) Next-Agent Prediction, which selects the most suitable agent role at each step, and (2) Next-Context Selection (NCS), which enables each agent to selectively access relevant information from any previous step. Together, these components construct task-adaptive communication pipelines that support both role flexibility and global information flow. Extensive evaluations across multiple benchmarks demonstrate that our approach achieves superior performance while substantially reducing communication overhead.

Paper Type: Long

Research Area: Machine Learning for NLP

Research Area Keywords: Large Language Models, Multi-Agent Collaboration

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Reproduction study

Languages Studied: English

Submission Number: 7291