BAMAS: Smarter AI Teams on a Budget
Smarter AI Teams, Smaller Bills: Meet BAMAS
Large language model (LLM) agent teams can tackle tough problems—but their cloud bills add up fast. BAMAS is a new method for building multi-agent systems that keeps performance high while respecting a budget.
How it works:
- Pick the right mix of models. BAMAS formulates an integer linear program to balance accuracy and cost when choosing which LLMs to include.
- Design how they talk. A reinforcement learning strategy selects the best interaction topology—who collaborates with whom and when.
- Run the plan. The selected agents and topology are instantiated and executed for the task.
In tests across three representative tasks, BAMAS matched state-of-the-art approaches while cutting costs by up to 86%.
Paper by Liming Yang, Junyu Luo, Xuanzhe Liu, Yiling Lou, and Zhenpeng Chen. Read more: https://arxiv.org/abs/2511.21572v1
Paper: https://arxiv.org/abs/2511.21572v1
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