标题： HASHIRU：用于混合智能资源利用的分层代理系统 显示英文标题

标题： HASHIRU: Hierarchical Agent System for Hybrid Intelligent Resource Utilization

摘要： 快速的大语言模型（LLM）进展正在推动自主多智能体系统（MAS）的发展。然而，当前的框架往往缺乏灵活性、资源意识、模型多样性以及自主工具创建能力。本文介绍了一种名为HASHIRU（混合智能资源利用的分层代理系统）的新颖MAS框架，该框架增强了灵活性、资源效率和适应性。HASHIRU具有一个“首席执行官”代理，动态管理专门的“员工”代理，这些代理根据任务需求和资源限制（成本、内存）实例化。其混合智能优先使用较小的本地LLM（通过Ollama），同时灵活地在必要时使用外部API和较大模型。一个带有雇佣/解雇成本的经济模型促进了团队稳定性和高效资源分配。该系统还包括自主API工具创建和记忆功能。在学术论文评审（58%成功率）、安全性评估（在JailbreakBench子集上达到100%）、复杂推理（在GSM8K上优于Gemini 2.0 Flash：96% vs. 61%；在JEEBench上：80% vs. 68.3%；在SVAMP上：92% vs. 84%）等任务上的评估表明了HASHIRU的能力。案例研究展示了其通过自主成本模型生成、工具集成和预算管理实现自我改进的能力。通过动态分层控制、资源感知的混合智能和自主功能扩展，HASHIRU为更强大、高效和适应性强的MAS提供了一个有前景的方法。源代码和基准测试分别可在https://github.com/HASHIRU-AI/HASHIRU 和https://github.com/HASHIRU-AI/HASHIRUBench获取，且可通过请求在https://hashiruagentx-hashiruai.hf.space 获取实时演示。 显示英文摘要

摘要： Rapid Large Language Model (LLM) advancements are fueling autonomous Multi-Agent System (MAS) development. However, current frameworks often lack flexibility, resource awareness, model diversity, and autonomous tool creation. This paper introduces HASHIRU (Hierarchical Agent System for Hybrid Intelligent Resource Utilization), a novel MAS framework enhancing flexibility, resource efficiency, and adaptability. HASHIRU features a "CEO" agent dynamically managing specialized "employee" agents, instantiated based on task needs and resource constraints (cost, memory). Its hybrid intelligence prioritizes smaller, local LLMs (via Ollama) while flexibly using external APIs and larger models when necessary. An economic model with hiring/firing costs promotes team stability and efficient resource allocation. The system also includes autonomous API tool creation and a memory function. Evaluations on tasks like academic paper review (58% success), safety assessments (100% on a JailbreakBench subset), and complex reasoning (outperforming Gemini 2.0 Flash on GSM8K: 96% vs. 61%; JEEBench: 80% vs. 68.3%; SVAMP: 92% vs. 84%) demonstrate HASHIRU's capabilities. Case studies illustrate its self-improvement via autonomous cost model generation, tool integration, and budget management. HASHIRU offers a promising approach for more robust, efficient, and adaptable MAS through dynamic hierarchical control, resource-aware hybrid intelligence, and autonomous functional extension. Source code and benchmarks are available at https://github.com/HASHIRU-AI/HASHIRU and https://github.com/HASHIRU-AI/HASHIRUBench respectively, and a live demo is available at https://hashiruagentx-hashiruai.hf.space upon request.