ATOM: Instantiating Budget-Controllable Multi-Agent Collaboration via Nucleus-Electron Hierarchy
arXiv:2605.26178v1 Announce Type: cross Abstract: Large Language Model (LLM)-based multi-agent systems rely on optimized collaboration topologies to balance performance and communication costs. However, current methods struggle with the inherent stability-extensibility trade-off and often misalign computational budgets with query difficulty. We propose \textsc{ATOM}, an adaptive framework that generates budget-controllable collaboration graphs via a novel task-driven reinforcement learning paradigm. Inspired by atomic structures, \textsc{ATOM} employs a nucleus-electron hierarchy: it maintains
The proliferation of advanced LLMs and multi-agent systems necessitates innovative solutions for managing communication costs and optimizing performance, driving research into adaptive collaboration frameworks.
This development addresses critical challenges in designing efficient and scalable multi-agent AI systems, enabling more complex and budget-controllable AI applications.
The ability to generate budget-controllable collaboration graphs allows for more tailored and resource-efficient deployment of multi-agent LLM systems, moving beyond one-size-fits-all topologies.
- · AI developers
- · Cloud providers
- · Enterprises adopting AI agents
- · Inefficient multi-agent system architectures
- · Organizations with high AI operational costs
Improved performance and cost-efficiency of LLM-based multi-agent systems.
Accelerated development and adoption of AI agents across various industries due to better scalability and resource management.
New competitive advantages for companies that master the deployment of highly efficient, budget-optimized AI agent ensembles.
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