OMAC Framework Introduced for Optimizing LLM-Based Multi-Agent Collaboration
A new framework called OMAC has been developed to systematically optimize multi-agent systems powered by large language models. Researchers identified five key dimensions for enhancing both agent functionality and collaboration structures. The framework employs a general algorithm involving two actors: a Semantic Initializer and a Contrastive Comparator. This approach aims to improve performance in complex tasks like code generation and arithmetic reasoning. The work addresses a gap in the literature, as current multi-agent systems often rely on handcrafted methods rather than systematic design. The announcement was made in a paper on arXiv with the identifier 2505.11765v3, categorized as a replace-cross type. Multi-agent systems have shown enhanced capabilities by enabling agents to collaborate and communicate. The development of OMAC represents a step toward more efficient and scalable AI-driven applications.
Key facts
- OMAC is a framework for holistic optimization of LLM-based multi-agent systems
- It identifies five key optimization dimensions for agent functionality and collaboration
- The framework uses a general algorithm with Semantic Initializer and Contrastive Comparator actors
- Multi-agent systems enhance capabilities in tasks like code generation and arithmetic reasoning
- Current systems often rely on handcrafted methods, lacking systematic design
- The paper was announced on arXiv with identifier 2505.11765v3 as replace-cross type
- Large language models power agents with impressive capabilities across diverse applications
- The work aims to address limited literature on systematic optimization of such systems
Entities
Institutions
- arXiv