MorphAgent: Empowering Agents through Self-Evolving Profiles and Decentralized Collaboration
Siyuan Lu, Jiaqi Shao, Bing Luo, Tao Lin
TL;DR
MorphAgent introduces a decentralized multi-agent system where agents self-evolve their profiles and roles, enhancing adaptability and performance in complex tasks without centralized control.
Contribution
It presents a novel framework enabling agents to dynamically evolve roles and capabilities through self-optimization, improving multi-agent collaboration.
Findings
Outperforms existing frameworks in task performance
Enhances adaptability to changing requirements
Demonstrates robustness in dynamic environments
Abstract
Large Language Model (LLM) based multi-agent systems (MAS) have shown promise in tackling complex tasks, but often rely on predefined roles and centralized coordination, limiting their adaptability to evolving challenges. This paper introduces MorphAgent, a novel Autonomous, Self-Organizing, and Self-Adaptive Multi-Agent System for decentralized agent collaboration that enables agents to dynamically evolve their roles and capabilities. Our approach employs self-evolving agent profiles, optimized through three key metrics, guiding agents in refining their individual expertise while maintaining complementary team dynamics. MorphAgent implements a two-phase process: a Profile Update phase for profile optimization, followed by a Task Execution phase where agents continuously adapt their roles based on task feedback. Our experimental results show that MorphAgent outperforms existing…
Peer Reviews
Decision·Submitted to ICLR 2025
- MORPHAGENT moves from predefined roles and centralized coordination to adaptive, fully decentralized coordination. - It defines three metrics to measure the guide the agent profile design. - Experiments on three benchmarks and ablation studies demonstrates improvements.
Frankly speraking, the paper's core contribution lies in the definition of three key metrics—Role Clarity Score (RCS), Role Differentiation Score (RDS), and Task-Role Alignment Score (TRAS)—to optimize agent profiles within a decentralized multi-agent system. I feel that this contribution is more like a prompting engieering technique, not enough to be an innovative point in an ICLR paper.
1. This paper identifies key challenges in multi-agent systems (MAS) and addresses them through decentralized and adaptive paradigms, with experiments demonstrating the effectiveness of this approach. 2. It introduces agent profiles as dynamic representations of evolving capabilities and responsibilities, using three quantitative metrics to evaluate and guide profile improvement. 3. Extensive experiments validate the proposed method, confirming its effectiveness and robustness.
1. While some algorithms are mentioned in the appendix, key details regarding their implementation and operation are not sufficiently clear. 2. The experiments are conducted only on two closed large language models (LLMs), which limits the generalizability of the findings. The exclusion of open-source models prevents a broader evaluation of the proposed method's effectiveness across diverse models. 3. This paper primarily considers agent profiles as dynamic representations of evolving
1. The paper effectively communicates its ideas through clear visualization - Figure 1 illustrates the key challenges with concrete examples, while Figure 2 provides a comprehensive overview of the framework's workflow. 2. The experimental results seem good, showing MorphAgent's consistent performance gain across different benchmarks. 3. Analyses of their framework's advantages is presented.
1. The implementation details and methodology are severely unclear and poorly explained: - The profile updating process is vaguely described, with crucial details buried in figures and appendix - The three metrics are defined with numerous undefined notations and unexplained components (e.g., *skill prototype* and *potential skill tokens* in Definition 3.1, and *vector representations* in Definition 3.3) - The design choices lack justification, such as using dependency trees in RCS -
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Taxonomy
TopicsModular Robots and Swarm Intelligence · Evolutionary Algorithms and Applications
MethodsMixing Adam and SGD