Allen: Rethinking MAS Design through Step-Level Policy Autonomy
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Abstract (arXiv)
We introduce a new Multi-Agent System (MAS) - Allen, designed to address two core challenges in current MAS design: (1) improve system's policy autonomy, empowering agents to dynamically adapt their behavioral strategies, and (2) achieving the trade-off between collaborative efficiency, task supervision, and human oversight in complex network topologies. Our core insight is to redefine the basic execution unit in the MAS, allowing agents to autonomously form different patterns by combining these units. We have constructed a four-tier state architecture (Task, Stage, Agent, Step) to constrain system behavior from both task-oriented and execution-oriented perspectives. This achieves a unification of topological optimization and controllable progress. Allen grants unprecedented Policy Autonomy, while making a trade-off for the controllability of the collaborative structure. The project code has been open source at: https://github.com/motern88/Allen
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References · 7
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
- 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
- 2025A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligencecited by 170
- 2025Multi-Agent Design: Optimizing Agents with Better Prompts and Topologiescited by 113
- 2024Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool Usagecited by 51
- 2024Agent Design Pattern Catalogue: A Collection of Architectural Patterns for Foundation Model based Agentscited by 35
- 2025Iterative Tool Usage Exploration for Multimodal Agents via Step-wise Preference Tuningcited by 19
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