Compound AI systems promise capabilities beyond those of individual models, yet their success depends critically on effective orchestration. Existing routing approaches face two limitations: (1) input-level routers make coarse query-level decisions that ignore evolving task requirements; (2) RL-trained orchestrators are expensive to adapt and often suffer from routing collapse, repeatedly invoking one strong but costly option in multi-turn scenarios. We introduce SkillOrchestra, a framework for skill-aware orchestration. Instead of directly learning a routing policy end-to-end, SkillOrchestra learns fine-grained skills from execution experience and models agent-specific competence and cost under those skills. At deployment, the orchestrator infers the skill demands of the current interaction and selects agents that best satisfy them under an explicit performance-cost trade-off. Extensive experiments across ten benchmarks demonstrate that SkillOrchestra outperforms SoTA RL-based orchestrators by up to 22.5% with 700x and 300x learning cost reduction compared to Router-R1 and ToolOrchestra, respectively. These results show that explicit skill modeling enables scalable, interpretable, and sample-efficient orchestration, offering a principled alternative to data-intensive RL-based approaches. The code is available at: https://github.com/jiayuww/SkillOrchestra.
ERSkill: Evolving for Skill-Guided Adaptive Memory Retrieval
Shenzhen International Center for Industrial and Applied Mathematics（深圳国际工业与应用数学中心）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Shenzhen Loop Area Institute（深圳河套学院）
Beyond Top-$k$ Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents
Virginia Tech（弗吉尼亚理工大学）；University of Southern California（南加州大学）；Adobe Research（Adobe研究院）；Dolby Labs（杜比实验室）；Texas A&M University（德克萨斯A&M大学）；Arizona State University（亚利桑那州立大学）
DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning
College of Intelligence Science and Technology, National University of Defense Technology（智能科学与技术学院，国防科技大学）；College of Computer Science and Technology, National University of Defense Technology（计算机科学与技术学院，国防科技大学）
Beyond Task-Only Matching: Personalized Skill Routing with Counterfactual Evaluation
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；College of Software Engineering, Southeast University（东南大学软件学院）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程学系）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
Kuaishou Technology（快手科技）；Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（教育部多媒体可信感知与高效计算重点实验室，厦门大学）；VCIP, CS, Nankai University（南开大学VCIP实验室）
