| Challenge: | Multi-agent systems (MAS) powered by Large Language Models (LLMs) have been demonstrated to push the boundaries of LLM capabilities, yet they often face significant costs and challenges in dynamic LLM selection. |
| Approach: | They propose a multi-agent system routing solution that integrates all components of MAS into a unified routing framework. |
| Outcome: | The proposed solution is high-performing, cost-effective, and efficient . it reduces overhead by up to 52.07 compared to current methods on HumanEval . |
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| Challenge: | Large Language Models (LLMs)-driven Multi-Agent Systems (MAS) have demonstrated remarkable scalability and generalizability across complex tasks. |
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| Challenge: | Existing models with limited performance and limited training can be difficult to use in large-scale applications. |
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| Challenge: | Existing routing methods suffer from poor scalability and dependence on datasets for training . energy footprint is also considered in the decision to implement our new LLM routing framework . |
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| Challenge: | Large language models (LLMs) have revolutionized natural language processing, but their practical implementation as autonomous multi-agent systems remains fraught with unresolved challenges. |
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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
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| Challenge: | Multi-agent systems powered by large language models still face challenges . tutorial focuses on three core components to build effective and efficient systems . |
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| Challenge: | Multi-turn, long-horizon tasks require dozens of sequential model calls per episode. |
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A Survey on LLM-powered Agents for Recommender Systems (2025.findings-emnlp)
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| Challenge: | Large Language Models have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation. |
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BiCSRouter: Bi-Level Cross-System Routing for Utility-Aware LLM Inference (2026.findings-acl)
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| Challenge: | Existing routing frameworks operate within a single computational paradigm . a cross-system routing framework that integrates two orthogonal regimes is proposed . |
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