针对预算约束，设计可低成本部署的多智能体系统架构。
AgentBalance: Backbone-then-Topology Design for Cost-Effective Multi-Agent Systems under Budget Constraints
- 先选不同大模型作为智能体基础，再优化通信结构。
- 在相同成本下，性能最高提升22%，兼顾效率与效果。
- 适合需控制成本的在线服务部署，兼容现有系统。
基于大语言模型（LLM）的多智能体系统（MAS）正成为网络级应用（如网页搜索、社交分析、在线客服）的核心组件，成本效益已成为大规模部署的关键约束。尽管近期工作通过调整智能体间通信拓扑和选择模型基础来提升成本效益，但很少在显式分词成本和延迟预算下建模与优化，导致常出现拓扑优先设计，在预算紧约束下表现不佳。本文提出AgentBalance框架，通过‘先基础后拓扑’的设计，在明确分词成本与延迟预算下构建高效多智能体系统。首先通过构建大模型池、筛选池并进行角色-模型匹配，生成具有异构基础的智能体；随后通过表示学习、门控机制与延迟感知拓扑合成实现自适应通信结构生成。在包含14个候选LLM的基准测试中，AgentBalance在匹配分词成本与延迟预算下分别实现最高10%和22%的性能提升，并在各基准上表现出优异的性能-预算曲线AUC值。该框架可作为插件无缝集成至现有MAS，在相同预算下提升性能，且对未见过的LLM具有良好泛化能力，适用于实际的预算敏感型部署。代码已开源：https://github.com/usail-hkust/AgentBalance。
原文摘要 · Abstract (English)
Large Language Model (LLM)-based multi-agent systems (MAS) are becoming indispensable building blocks for web-scale applications such as web search, social network analytics, and online customer support, where cost-effectiveness is increasingly the primary constraint for large-scale deployment. While recent work improves MAS cost-effectiveness by shaping inter-agent communication topologies and selecting agent backbones, it rarely models and optimizes under explicit token-cost and latency budgets that reflect deployment constraints. This often leads to topology-first designs and suboptimal cost-effectiveness when budgets are binding. We present AgentBalance, a framework for constructing cost-effective MAS under explicit token-cost and latency budgets via a backbone-then-topology design. AgentBalance first performs backbone-oriented agent generation, constructing agents with heterogeneous backbones through LLM pool construction, pool selection, and role-backbone matching. It then performs adaptive MAS topology generation, guiding inter-agent communication via agent representation learning, gating, and latency-aware topology synthesis. Experiments on benchmarks with 14 candidate LLM backbones show that AgentBalance achieves up to 10% and 22% performance gains under matched token-cost and latency budgets, respectively, and yields strong AUC on performance-versus-budget curves across benchmarks. AgentBalance also functions as a plug-in for existing MAS, improving performance under the same token-cost and latency constraints, and it generalizes well to unseen LLMs for practical, budget-aware deployment. Code: https://github.com/usail-hkust/AgentBalance
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