MANTA: Multi-Agent Network Topology Adaptation for Self-Evolving Multi-Agent Systems

Imagine you're building a team of AI agents to tackle complex tasks, like planning a project or solving a puzzle. The challenge isn't just getting them to work together; it's ensuring they can adapt their communication and roles as the task evolves. Current systems often set communication patterns in stone, which can lead to inefficiencies when the situation changes — this is what's called a fixed topology problem. If agents can't adjust their interactions, they might miss critical information or fail to coordinate effectively, leading to suboptimal outcomes. To address this, MANTA introduces a framework that allows agent communication structures to evolve during execution. It starts by establishing a task-specific communication topology based on past experiences. As the agents work, MANTA monitors their collaboration and can make real-time adjustments to roles, communication links, and information flow when it detects that the current setup isn't working. This flexibility means that agents can better handle unexpected challenges and improve their performance on the fly. Compared to traditional methods, MANTA's ability to adapt communication dynamically represents a significant step forward, making it more suitable for real-world applications where conditions can change rapidly.

MANTA introduces a dynamic approach to agent communication that adapts in real-time.

The evaluation against multiple baselines and benchmarks supports its effectiveness.