Multi-Agent Systems(MAS) have become a powerful paradigm for building high performance intelligent applications. Within these systems, the router responsible for determining which expert agents should handle a given query plays a crucial role in overall performance. Existing routing strategies generally fall into two categories: performance routing, which balances latency and cost across models of different sizes, and task routing, which assigns queries to domain-specific experts to improve accuracy. In real-world enterprise applications, task routing is more suitable; however, most existing approaches rely on static single-label decisions, which introduce two major limitations: (i) difficulty in seamlessly integrating new agents as business domains expand, and (ii) routing conflicts caused by overlapping agent capabilities, ultimately degrading accuracy and robustness.To address these challenges, we propose TCAndon-Router(TCAR): an adaptive reasoning router for multi-agent collaboration. Unlike traditional routers, TCAR supports dynamic agent onboarding and first generates a natural-language reasoning chain before predicting a set of candidate agents capable of handling the query. In addition, we design a collaborative execution pipeline in which selected agents independently produce responses, which are then aggregated and refined into a single high-quality response by a dedicated Refining Agent.Experiments on public datasets and real enterprise data demonstrate that TCAR significantly improves routing accuracy, reduces routing conflicts, and remains robust in ambiguous scenarios. We have released TCAR at https://huggingface.co/tencent/TCAndon-Router to support future research on explainable and collaborative multi-agent routing.
Pro-Router: Token-Aware Progressive Model Routing with Adaptive Edge-Cloud Collaboration for Efficient Multimodal LLM Inference
Anyscale（安尼斯科尔公司）；Mississippi State University（密西西比州立大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Institute of AI for Industries, Chinese Academy of Sciences（中国科学院产业人工智能研究院）
MoE Router-Guided Clustering for Heterogeneous Federated Instruction Tuning
California State University, Dominguez Hills（加州州立大学多明戈斯山分校）；University of California, Irvine（加州大学欧文分校）；Johns Hopkins University（约翰斯·霍普金斯大学）；Arizona State University（亚利桑那州立大学）
Collaboration! Towards Robust Neural Methods for Routing Problems
Nanyang Technological University（南洋理工大学）；Eindhoven University of Technology（埃因霍温理工大学）；Singapore Management University（新加坡管理大学）；Shandong University（山东大学）
CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing
The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Intel（英特尔）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·拉希德人工智能大学）；Rutgers University（罗格斯大学）
MedCoRAG: Interpretable Hepatology Diagnosis via Hybrid Evidence Retrieval and Multispecialty Consensus
School of Cyber Science and Engineering, Nanjing University of Science and Technology（南京理工大学信息科学与工程学院）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Department of Neurology, China-Japan Union Hospital of Jilin University（吉林大学中日联谊医院神经内科）；Department of Anesthesiology, China-Japan Union Hospital of Jilin University（吉林大学中日联谊医院麻醉科）；School of Computing, Macquarie University（麦考瑞大学计算机学院）
A Versatile AI Agent for Rare Disease Diagnosis and Risk Gene Prioritization
Interdepartmental Program in Computational Biology and Bioinformatics, Yale University（耶鲁大学计算生物学与生物信息学联合计划）；Department of Biostatistics, Yale University（耶鲁大学生物统计学系）；Broad Institute of MIT and Harvard（哈佛大学与麻省理工学院联合Broad研究所）；Center for Biomedical Data Science, Duke-NUS Medical School（杜克-新加坡医学学校生物医学数据科学中心）；Sport and Exercise Medicine Service, KK Women’s and Children’s Hospital Training Program, Duke-NUS Medical School（杜克-新加坡医学学校KK妇女儿童医院运动与医学服务培训项目）；Training Program, Duke-NUS Medical School（杜克-新加坡医学学校培训项目）；Department of Computer Science and Engineering, The Ohio State University（俄亥俄州立大学计算机科学与工程系）；Department of Complexity Science and Engineering, The University of Tokyo（东京大学复杂科学与工程系）；Center for Advanced Intelligence Project, RIKEN（日本理化学研究所高级智能项目中心）；NUS Artificial Intelligence Institute, National University of Singapore（新加坡国立大学人工智能研究所）；Department of Biostatistics and Bioinformatics, Duke University（杜克大学生物统计学与生物信息学系）；Department of Biomedical Data Science, Stanford University（斯坦福大学生物医学数据科学系）；Department of Genetics, Yale University（耶鲁大学遗传学系）；Wu Tsai Institute, Yale University（耶鲁大学吴天教授研究所）；Department of Biomedical Informatics and Data Science, Yale University（耶鲁大学生物医学信息学与数据科学系）
