Multi-agent systems improve task coordination with coherent memory and roles
Topological Coherence for Self-evolving Multi-agent Systems
Artificial Intelligence
Summary
Coordinating many software agents to work together on complex tasks requires clear roles, memory sharing rules, and handoffs between parts of the task. The authors highlight a concept called topological coherence, ensuring these boundaries fit the task's structure. They created TOCOMAS, a system that organizes agents and memory based on task dependencies and evolves these setups during use, improving success on benchmark tests. This helps multi-agent systems work more reliably and efficiently over time.
What this means in practice
- •For autonomous robotics teams: Organize robots' task roles and shared memory to improve coordination and task success in evolving environments.
- •For software architects: Design self-adapting multi-agent software that maintains coherent responsibility and communication boundaries for complex workflows.
Authors
Sen Zhao, Ruiqi Kong, Zuyu Zhang, Lifeng Shen, Xinyu He, Xu Zhang, Qinghua Zhang
Abstract
Complex tasks inherently couple workflow structure, agent responsibility, collaboration, and memory access: task regions delimit responsibility and tool scope, cross-region dependencies give rise to handoffs, and ownership boundaries delimit private and selectively shared memory. Existing methods can jointly optimize agent and communication structures, yet such optimization does not by itself require responsibility, handoff, and memory boundaries to remain consistent with task dependencies. We term this requirement topological coherence. We introduce TOCOMAS, a Topology-Coherent Multi-Agent System. TOCOMAS grounds a task graph in tool interfaces, organizes compatible task nodes into reusable responsibility domains, and derives dependency-induced and profile-conditioned collaboration together with boundary-regulated memory visibility. During online self-evolution, TOCOMAS proposes coupled changes to agent, collaboration, and memory policies, retaining for subsequent tasks only candidates that satisfy structural constraints and improve evaluated reward. Across BBEH, WorkBench, SWE-Bench-Verified, and CoMemBench, TOCOMAS improves task success over baselines across backbones. CoMemBench also shows gains over the self-evolving baseline in verified progress, handoffs, and memory isolation.