Multi-agent Architecture Search via Agentic Supernet
- Published
- Source
- arXiv
- Paper number
- 031
- Field
- Agents
- arXiv ID
- 2502.04180
Key points
- Traditional multi-agent system (MAS) design is labor-intensive and requires extensive manual prompt engineering and complex agent-to-agent communication definitions.
- Current automated MAS design methods usually search for a single, uniform optimal system, which leads to resource inefficiency and high cost on simpler queries.
- A single optimal MAS often lacks adaptability across different task domains, so it can degrade or run inefficiently when transferred without re-optimization.
- MaAS introduces an agent supernet, a stochastic L-layer directed acyclic graph (DAG) built from agent operators, from which customized multi-agent structures can be sampled.
- The controller network uses MoE-style networks and early-exit operators to manage computation depth according to query complexity and dynamically sample query-specific structures from the supernet.
- The system uses Monte Carlo methods to optimize the operator distribution in the supernet and refines individual agent operators through agent-based text gradients, balancing performance and token cost.
Paper links
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