Multi-agent Architecture Search via Agentic Supernet

Code

• github.com/bingreeky/MaAS
  Officialnone★ 253

Abstract

Large Language Model (LLM)-empowered multi-agent systems extend the cognitive boundaries of individual agents through disciplined collaboration and interaction, while constructing these systems often requires labor-intensive manual designs. Despite the availability of methods to automate the design of agentic workflows, they typically seek to identify a static, complex, one-size-fits-all system, which, however, fails to dynamically allocate inference resources based on the difficulty and domain of each query. To address this challenge, we shift away from the pursuit of a monolithic agentic system, instead optimizing the agentic supernet, a probabilistic and continuous distribution of agentic architectures. We introduce MaAS, an automated framework that samples query-dependent agentic systems from the supernet, delivering high-quality solutions and tailored resource allocation (e.g., LLM calls, tool calls, token cost). Comprehensive evaluation across six benchmarks demonstrates that MaAS (I) requires only 645\% of the inference costs of existing handcrafted or automated multi-agent systems, (II) surpasses them by 0.54\%11.82\%, and (III) enjoys superior cross-dataset and cross-LLM-backbone transferability.

Tasks

Reproductions