动态分配模型，让复杂任务更省算力还高效
CASTER: Breaking the Cost-Performance Barrier in Multi-Agent Orchestration via Context-Aware Strategy for Task Efficient Routing
- 用语义+结构特征判断任务难易，智能选模型
- 推理成本降72.4%，成功率与强模型持平
- 适合需要高效多智能体协作的工程与科研场景
基于图的多智能体系统（MAS）可支持复杂循环工作流，但静态模型部署导致在简单子任务上浪费大量计算资源。本文提出轻量级路由框架CASTER（上下文感知的任务高效路由策略），通过双信号路由器结合语义嵌入与结构元特征来估计任务难度。训练阶段采用从冷启动到迭代演化的机制，利用自身路由失败的在线策略负反馈自我优化。在软件工程、数据分析、科学发现和网络安全四个领域，基于大模型评分的实验表明，CASTER相比强模型基线最多降低72.4%的推理成本，且成功率相当；同时显著优于启发式路由与FrugalGPT，在所有领域均表现更优。
原文摘要 · Abstract (English)
Graph-based Multi-Agent Systems (MAS) enable complex cyclic workflows but suffer from inefficient static model allocation, where deploying strong models uniformly wastes computation on trivial sub-tasks. We propose CASTER (Context-Aware Strategy for Task Efficient Routing), a lightweight router for dynamic model selection in graph-based MAS. CASTER employs a Dual-Signal Router that combines semantic embeddings with structural meta-features to estimate task difficulty. During training, the router self-optimizes through a Cold Start to Iterative Evolution paradigm, learning from its own routing failures via on-policy negative feedback. Experiments using LLM-as-a-Judge evaluation across Software Engineering, Data Analysis, Scientific Discovery, and Cybersecurity demonstrate that CASTER reduces inference cost by up to 72.4% compared to strong-model baselines while matching their success rates, and consistently outperforms both heuristic routing and FrugalGPT across all domains.
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